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H S Migon and
D Gamerman ""\ ::1'
Federal University of Rio de Janeiro, Brazil
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ARNOLD
A member of the Hodder Headline Group LONDON
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NEW YORK
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SYDNEY
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AUCKLAND
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First published in Great Britain in 1999 by Arnold, a member of the Hodder Headline Group, 338Euston Road, London N W l 3BH http://www.arnoldpublishers.com Copublished in the United States of America by Oxford University Press Inc., 198Madison Avenue, New York, NY 10016 © 1999 Dani Gamerman and Helio Migon All rights reserved. No part of this publication may be reproduced or transmined in any form or by any means, electronically or mechanically, including photocopying, recording or any information storage or retrieval system, without either prior permission in writing frorri the publisher or a liCenCe permitting restricted copying. In the United Kingdom such licences are issued by the Copyright Licensing Agency: 90 Tottenham Court Road, London W 1 P 9HE. Whilst the advice and information in this book are believed t.o be true and accurate at the date of going
to press, neither the authors nor the publisher can accept any legal responsibility or liability for any errors or omissions that may be made.
..
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•.
Para Mirna, Marcio, Marcelo e Renato (Helio) Para meus pais Bernardo e Violeta (Dani)
Contents
Preface I
2
3
4
vii
In.troduction
I
1.1
Information
2 3
1.2
The concept of probability
1.3
An example
1.4
Basic results in linear algebra and probability
10
1.5
Notation
14
1.6
Outline of the book
15
Exercises
16
Elements of inference
7
I9
2.1
Common statistical models
19
2.2
Likelihoodbased functions
21
2.3
Bayes' theorem
26
2.4
Exchangeability
32
2.5
Sufficiency and the exponential family
35
2.6
Parameter elimination
41
Exercises
45
Prior distribution
53
3.1
Entirely subjective specification
53
3.2
Specification through functional fonns
55
3.3
Conjugacy with the exponential family
59
3.4
The main conjugate families
60
3.5
Noninformative priors
65
3.6
Hierarchical priors
71
Exercises
73
Estimation
79
4.1
Introduction to decision theory
4.2
Classical point estimation
86
4.3
Comparison of estimators
92
79
vi Contents 4.4
Interval estimation
4.5
Estimation in the normal model Exercises
5
6.
116
Approximate and computationally intensive methods
8
125
5.1
The general problem of inference
5.2
Optimization techniques
126
5.3
Analytical approximations Numerical integration methods
133
5.4 5.5
Simulation methods
147
Exercises
160
Hypothesis testing
125
144
167
6.1
Introduction
6.2
Classicai h
168
6.3
Bayesian hypothesis testing
181
6.4
Hypothesis testing and confidence intervals Asymptotic tests
186
6.5
yi)othesis testing
Exercises 7
99 105
Prediction
167
188 191 197
7.1
Bayesian prediction
197
7.2
Classical prediction
201
7.3
Prediction in the normal model
203
7.4
Linear prediction
206
Exercises
208
Introduction to linear models
211
8.1
The linear model
211
8.2
Classical linear models
213
8.3
Bayesian linear models
218
8.4
Hierarchical linear models
224
8.5
Dynamic linear models
227
Exercises
232
Sketched solutions to selected exercises
235
List of distributions
247
References
253
Author index
256
Subject index
258
Preface
This book originated from tQ'le lecture notes of a course in statistical inference taught on the MSc programs in Statistics at UFRJ and IMPA (once). These notes have been used since 1987. During this period, various modifications were introduced until we arrived at this version, judged as minimally presentable. The motivation to prepare this book came from two different sources. The first and more obvious one for us was the lack of texts in Portuguese, dealing with statistical inference to the desired depth. This motivation Jed us to prepare the first draft of this book in the Portuguese language in 1993. The second, and perhaps the most attractive as a personal challenge, was the perspective adopted in this text.
Although there are various good books in the literature dealing with this
subject, in none of them could we find an integrated presentation of the two main schools of statistical thought: the frequentist (or classical) and the Bayesian.. This second motivation led to the preparation of this English version. This version has substantial changes with respect to the Portuguese version of 1993. The mOst notable one was the inclusion of a whole new chapter dealing with approximation and computationally intensive methods. Generally, statistical books follow their author's point of view, presenting at most, and in separate sections, related results from the alternative approaches. In this book, our proposal was to show, wherever possible, the parallels existing be tween the results given by both methodologies. Comparative Statistical Inference by V. D. Barnett (1973) is the book that is closest to this proposal. It does not however present many of the basic inference results that should be included in a text proposing a wide study of the subject. Also we wanted to be as comprehensive as possible for our aim of writing a textbook in statistical inference. This book is organized as follows.
Chapter l is an introduction, describing
the way we find most appropriate to think statistics: discussing the concept of information.
Also, it briefly reviews basic results of probability and linear al
gebra. Chapter 2 presents some basic concepts of statistics such as sufficiency, exponential family, Fisher infonnation, exchangeability and likelihood functions. Another basic concept specific to Bayesian inference is prior distribution, which is separately dealt with in Chapter 3. Certain aspects of inference are individually presented in Chapters 4, 6, and 7. Chapter 4 deals with parameter estimation where, intentionally point and interval
viii Preface estimation are presented as responses to the summarization question, and not as
two unrelated procedures. The important results for the normal distribution are presented and also serve an illustrative purpose. Chapter 6 is about hypotheses testing problems under the frequentist approach and also under the various possible
forms of the Bayesian paradigm.
In between them lies Chapter 5 where all approximation and computationally based results are gathered. The reader will find there at least a short description of the main tools used to approximately solve the relevant statistical problem for situations where an explicit analytic solution is not available.
For this reason,
asymptotic theory is also included in this chapter. Chapter 7 cove(s prediction from both the frequentist and Bayesian points of view, and includes the linear Bayes method. Finally in Chapter 8, an introduction to normal linear models is made..initially the frequentist approach is presented, followed by the Bayesian one. Bas�;d upon the latt�r approach, generalisations are presented leading to the hierarchical and dynamic models. We tried to develop a critical analysis and to present the most important reSults
of both. approaches commenting on the positive and negative aspects of both. As
has already been said,_ the level of this book is adequate for an MSc course in
statistics, althoUgh· we do not rule out the possibility of its use in an advanced undergraduate course aiming to compare the two approaches.
This boo� can also be useful for the more mathematically trained profession als from related areas of science such as economics, mathematics, engineering,
operations research and epiderriiology. The basic requirements are knowledge of
calculus and probability, although basic notions of linear algebra are also used.
As this book is intended as .a basic text in statistical inference, various exercises are included at the end of each chapter. We have also included sketched solutions
to some of the exercises and a list of distributions at the end of the book, for easy
reference.
There are many possible uses of this book as a textbook. The first and most
obvious one is to present a11 the material in the order it appears in the book and
without skipping sections. This may be a heavy workload for a one semester
course. In this case we suggest postponing Chapter 8 to a later course. A second
option for exclusion in a first course is Chapter 5, although we strongly recommend it for anybody interested in the modem approach to statistics, geared towards applications. The book can also be used as a text for a course that is more strongly oriented towards one of the schools of thought. For a Bayesian route, follow
Chapters I, 2, 3, Sections 4.1, 4.4.1 and 4.5, Chapter 5, Sections 6.3, 6.4, 6.5,
7.1, 7.3.1, 7.4, 8.1, 8.3, 8.4 and 8.5. For a classical route, follow Chapter I, Sections 2.1, 2.2, 2.5, 2.6, 4.2, 4.3, 4.4.2 and 4.5, Chapter 5, Sections 6.1, 6.2, 6.4, 6.5, 7.2, 7.3.2, 7.4, 8.1 and 8.2.
This book would not have been possible without the cooperation of �various
people. An initial and very important impulse was the typing of the original lecture
notes in TE(( by Ricardo Sandes Ehlers. Further help was provided by Ana Beatriz
Soares Monteiro, Carolina Gomes, Eliane Amiune Camargo, Monica Magnanini
Preface ix and Ot 0 there could well exist an No > N such that IPr(A) (m/No)l > E. This is improbable but not impossible. (ii) The concepts of identical and independent trial are not easy to define objectively and are in essence subjective. (iii)
does not go to infinity and so there is no way to ensure the existence of such a limit.
n
The scope of the two interpretations is limited to observable events and does not correspond to the concept used by common people. The human being evaluates (explicitly or implicitly) probabilities of observable and unobservable events.
Ex I then p2 > I and (p 1)2 > 0. Therefore the losses are always bigger than I and 0, the losses obtained by making p = I. A similar argument is used to show that if p < 0 the losses wi11 be bigger than those evaluated with p = 0. Then. minimization of the square error losses imposes p E [0, I]. Figure 1 .1 arrives at the same conclusion graphically. 
6
Introduction 8
D C
0
Fig. 1.1 The possible losses are given by BD2 and C D2 which are minimized if D is between B and C. 2.
Pr (A)= I Pr(A) The possible losses associated with the specification of
Pr(A)
p and
Pr(A) = q are: A = I : (p 1)2 + q2 A= 0 : p2 + (q 1)2. As·we)mve already seen in
(I), the possible values of (p, q) are in the
unit square. In Figure 1.2 line segments are drawn to describe the possible losses. The squared distance between two consecutive vertices represents the losses. It is clear from the figure that the losses are reduced by making p + q=l.
q
p
Fig.1.2 Thepossible lossesaregiven by BD2 when A= I andCD2 when A= 0, which are minimized if D is projected on E over the line p + q = I. 3.
Pr(A n F)= Pr(A I F)Pr(F) Define Pr(A I F) as the probability of A ifF= I. Denoting this probability by p, Pr(F) by q and Pr(AnF) by r, the total loss is given by (pA)2 F + (q F)2 + (r AF)2. Its; possible values are: A= F = I : (p 1)2 + (q
 1)2 + (r 1)2
A = 0, F= I : p2 + (q I)2 + r2
An example 7
Note that
(p, q, r) assume values in the unit cube. The same arguments
used in (2) can be developed in the cube. Minimization of the three losses is attained when
1.3
p
/q.
= r
An example
In this section a simple example will be presented with the main intention of anticipating many of the general questions to be discussed later on in the book. The problem to be described is extremely simple but is useful to illustrate some relevant
ideas involved in statl stical reasoning. Only very basic concepts on probability are enough for the reader to follow the classical and Bayesian inferences we will present. The interested reader is recorrunended to read the excellent paper by Lindley and Phillips
(1976) for further discussion.
On his way to university one morning, one of the authors of this book (say, Helio) was stopped by a lady living in the neighbouring Mare slum. She was pregnant and anxious to know the chance of her seventh baby being male. Initia1ly, his reaction was to answer that the chance is 1/2 and to continue his way to work. But the lady was so disappointed with the response that Helio decided to proceed as a professional statistician would. He asked her som� questions about her private life and she told him that her big family was composed offive boys (M) and one girl (F) and the sequence in which the babies were born was MMMMMF. The lady was also emphatic in saying that all her pregnancies were consequence of her long relationship with the same husband. In fact, her disappointment with the naive answer was now understandable. Our problem is to calculate the chance of the seventh baby being male, taking into consideration the specific experience of this young lady. How can we solve this problem? Assuming that the order of the Ms and Fs in the outcome is not relevant to our analysis, it is enough or sufficient to take note that she had exactly five baby boys
(5 Ms) and one baby girl (I F). The question about the order of the births is
brought up because people usually want to know if there is any sort of abnormality in the sequence. However, it seems reasonable to assume the births to be equally distributed in probabilistic terms. Before proceeding with an analysis it is useful to define some quantities. Let us denote by X; the indicator variable of a boy for the ith child, i
=
let B denote the common unknown probability of a boy, i.e., Pr(X; with 0 :::
B
I, ... , 7 and =
liB)
=
B
::: I. Note that B is a fixed but unknown quantity that does not exist in
reality but becomes a useful summary of the situation under study. A frequentist statistician would probably impose independence between the X;'s. In doing that the only existing link between the X;'s is provided by the value of B. It seems reasonable at this stage to provide to the lady the value that one considers the most reasonable representation of 8. Given that he is only allowed to
8 Introduction use the observed data in his analysis, this representative value of e must be derived
from the observations X;, i = 1, . .. , 6.
There are many possible ways to do that.
Let us start by the probabilistic
description of the data. Given the assumptions above, it is not difficult to obtain
Pr(Xt = l,X2= l , ... ,Xs = l,X6=0ie) =e5(1e). One can proceed on this choice of value for e by finding the single value
{;
that
maximizes the above probability for the actual, observed data. It is a simple exercise to verify that, in the above case, this value is given by {J = 5/6 = 0.83. We would then say that she has 83% chance of giving birth to a boy. There are other w'Vs to proceed still based only on the observed data but more assumptions are now needed. One possible assumption is that the lady had pre viously decided that she only wanted to have six children, the last one being an unwanted pregnancy. In this case, the observed data can be summarized into the number Y of M's among the six births. It is clear that Y has a binomial distribu tion with size six and success probability e, denoted by Y bin(6, e), and that E(Y/6161) = e. Using frequentist arguments, one can reason that when we are �
able to observe many boys in a similar situation one would like to be correct on average. Therefore, one would estimate the value of(} as
Yj6, the relative fre
quency of boys in the data. Given that the observed value of Y is 5, a reasonable estimate for 61 is if= 5j6, coinciding with§. There is no guarantee that the two approaches coincide in general but is reassuring that they did in. thfs ·case.
When asking the lady if the assumption to stop at the sixth child was true, she
said that her decision had to do with having had her first baby girl. In th�s case, the observed data should have been summarized by the number
Z of M's she had Z and Y
until the first girl, and not by Y. (Even though the observed values of
are the same, their probability distributions are not.) It is not difficult to see that
Z has a negative binomial distribution with size 1 and success probability 1e, N 8(1, e), and that E(Zie) e ((! e). Proceeding on Z with the reasoning used for Y leads to the estimation of e by 5/6 as in the previous cases. denoted z
�
=
The main message from this part of the example for choosing a representative value for e is that there are many possible methods, two of which were applied above and while the first one did not depend on the way the data was observed, the second one did. These issues are readdressed at greater length in Chapters 2 and 4. Another route that can be taken is to decide whether it is reasonable or not to discard 1/2 as a possible value for 61. This.. can be done by evaluating how extreme
(in discordance of the assumption e = 1(2) the observed value is. To see that one can evaluate the probabilities that Y � 5 and
Z � 5, depending on which stopping
rule was used by tbe lady. The values of these probabilities are respectively given by 0.109 and 0.031. It is generally assumed that the cutoff point for measuring
extremeness in the data is to have probabilities sma11er than 0.05. It is interesting that in this case the stopping rule has a strong effect on the decision to discard the
equal probabilities assumption. This will be readdressed in a more general form
in Chapter 6.
An example 9 Intuition, however, leads to the belief that specification of the stopping rule is
not relevant to solving our problem. This point can be more formally expressed
in the following way: the unique relevant evidence is that in six births, the sex of the babies was honestly annotated as
1 F and 5 Ms. Furthermore, these outcomes
occurred in the order specified befote. This statement points to the conclusion that only the results that have effectively been observed are relevant for our analysis.
For a Bayesian statistician, the elements for the analysis are only the sequence of
the observed results and a probability distribution describing the initial information about the chance of a baby being male. The experimental conditions were carefully
described before and they guarantee that a result observed in any given_birth is equivalent to that obtained in any other birth. The same is true for pairs, triplets, etc.
of birth. This idea is formalized by the concept of exchangeability. The sequence of births is exchangeable if the order of the sex QUtcomes in the births is irrelevant.
In the next chapter, we will define precisely the concept of exchange?biHty. For our present example thi� means that the probability of any seqUefl�e ·of
s
Fs (subject to r + s
r
Ms and
= n) is the same as that of any other sequence with the same
number of Ms and Fs.
Let us return to our origi�al problem, that is, to calculate the chance of the seventh
baby born being male based on the information gathered. namely that provided by
the previous births. This probability is denoted by pair (5,
Pr[X, = 11(5, I)] where the 1) denote the number ofbirths from each sex previously observed. Using
basic notions o.f probability calculus we can obtain
Pr[X7 =
11(5, I)]= f' o J =
=
fo'
fa'
.
P[X7 = I,el(5, !)]de P[X,
=
11e . (5, l)]p(el(5, !)) de
ep(el(5, I)) de
= E[e I (5, !)] where the expected value is with respect to the distribution of 8 given the past
results. As we will see in the next chapter, this is the unique possible representation
for our problem if the assumption of exchangeability of the sequences of births is
acceptable. One of the elements involved in the above calculation is
p(el(5, I))
which has not yet been defined. It has the interpretation, under the subjective
approach, of the probability distribution of the possible values fore after observing the data (5, 1).
Let us suppose that before observing the values of
(5, 1), the subjective proba
bility specification for() can be represented by the density
o s e s I, which is a beta distribution with parameters
at the end of the book).
(a,b>O)
a and b (see the list of distributions
10
Introduction
Note that
p(8 I
(5 I))= '
8)
p((5, 1),
I)) p((5, I) I 8)p(8) = p((5, I)) s e)8at (I ex 8 0 p((5,
_
_
8)bt,
since p((5, I)) does not depend on ex
85+al (I
_
8
8) I +b1
and the stopping rule or the sample space, in cltJ.ssical language, is irrelevant because it gets incorporated into the proportionality constant. Furthermore, for any experimental result the final distribution will be a beta. So we can complete the calculations to obtain
Pr[X7
=I I (5, I)]=
E[8](5, I)]=
a+5 a+b+6
.
We still have a problem to be solved. What are the values of a and b? Suppose, in the case of births of babies, that our initial opinion about the chances associated with M and F are symmetric and concentrated around 0.5. the distribution of
e
This means that
is symmetrically distributed with mean 0.5 and with high
probability around the mean. We can choose in the family of beta distributions that one with a = b = 2, for instance. ?(0.4
C::)
t::)
0 for a positive (nonnegative) matrix A allows one to denote by A
B the fact that the matrix A  B >
(2:)
>
0. This ordering makes sense in
the context of probability. Let A and B be the variancecovariance matrices of
independent pdimensional random vectors X and Y. Then A > B implies that
14
Introduction
V (b'Y) =b' Ab b'B b=b' (A B) b > 0, for every nonnull vector V (b'X) b. So, there is a sense in which matrices can be compared in magnitude and one can say that A is larger than B. 
Symmetric positivedefinite matrices are said to be nonsingular, because they have a nonnull determinant. This implies that they have full rank and all their rows are linearly independent. Likewise, singular matrices have null determinant, which means that they do not have fuH rank and some of their rows can be represented as linear combinations of the other rows. Nonsingular matrices also have a well defined inverse matrix.
A square matrix A of order p with pdimensional rowsai
i
=
I, . .. , p is said to be orthogonal ifa;aj
that if A is orthogonal then A' A=A
=
=
(ai I· .. , aip)', for i' j. Note .
I, if i =j and 0, ifi
A'= lp. Therefore, orthogonal matrices can
be shown to be full rank with inverse A l and A' =A  l . There are many methods available in linear algebra for iteratively constructing an orthogonal matrix from
given starting rows. In most statistical applications, matrices are nonnegative definite and symmet
ric. The square root matrix of A denoted by A112, can then be defined and satisfies
A1i2A1i2 =A. One of the most common methods of funding the square root matrix is called the Choleski decomposition.
1.5
Notation
Before effectively beginning the study of statistical inference it will be helpful to make some general comments. Firstly, it is worth noting that here we will deal with regular models only, that is, the random quantities involved in our models are of the discrete or continuous type. A unifying notation will be used and the distinction will be clear from the context. Then, if
X is a random vector with
distribution function denoted by F(x), its probability (or density) function will be denoted by p(x) if
X is discrete (or continuous) and we will assume that
j independently of
dF (x)
=
j
p(x)dx
X being continuous or discrete, with the integral symbol repre
senting a sum in the discrete case. In addition, as far as the probability (or density) function is defined from the distributio� of
X, we will use the notation
X
.......,
p
meaning that X has distribution p or, being more precise, a distribution whose probability (or density) function is p. Similarly,
X
.!!...
p will be used to denote
that X converges in distribution to a random variable with density p.
In general, the observables are denoted by the capital letters of the alphabet (X,
Y, ...), as usual, and their observed values by lowercase letters (x, y, . . .). Known
(A, B, ..), and the greek (0, A, . .) are used to describe unobservable quantities. Matrices, observed
quantities are denoted by the first letters of the alphabet
letters
.
.
or not, will be denoted by capitals. Additionally, vectors and matrices will be
Outline of the book 15 distinguished from scalars by denoting the first ones in bold face. Results will generally be presented for the vector case and whenever the specialization to the scalar case is not immediate, they will be presented again in a univariate version. The distribution of
X is denoted by P(X). The expected value ofXIY is denoted E(XIY), Ex1y(X) or even Ex1y(XIY) and the variance of XIY is denoted by V(XIY), Vx1y(X) or even Vx1y(XIY). The indicator function, denoted by Ix(A), assumes the values by
fx(A)
1.6
=
·j1•
0,
if X E
A
otherwise.
Outline of the book
The purpose of this book is . Y:> pr.esent an integrated approach to statistical inference
at an inteimediate level b)r disCussion and comparison of the most important results
of the two main schools of statistical thought: frequentist and Bayesian. With that
. in mind, the results are presented for rnultipararneter models. Whenever needed, the special case of a single parameter is presented and derivations are sometimes made at this level to help understanding. Also, most of the examples are presented
at this level. It is hoped that they will provide motivation for the usefulness of the
results in the more general setting. Presentation of results for the two main schools of thought are made in parallel as·mtich as possible. Estimation and prediction are introduced with the Bayesian approach followed by the classical approach.
The presentation of hypothesis
testing and linear models goes the other way round. All chapters., including this intfoduction, contain a set of exercises at the end.
These are included to help
the student practice his/her knowledge of the material covered in the book. The exercises are divided according to the section of the chapter they refer to, even
though many exercises contain a few items which cover a few different sections. At the end of the book, a selection of exercises from all chapters have their solution presented. We tried to spread these exercises evenly across the material contained in the chapter. The material of the book will be briefly presented below. The book is composed of eight chapters that can broadly be divided into three parts. The first part contains the first three chapters introducing basic concepts needed for statistics. The second
part is composed at Chapters 4, 5 and 6 which discuss in an integrated way the
standard topics of estimation and hypothesis testing. The final two chapters deal with other important topics of inference: prediction and linear models. Chapter 1 consisted of an introduction with the aim of pioviding the flavour of
and intuition for the task ahead. In doing so, it anticipated at an elementary level many of the points to be addressed later in the bqok.
Chapter 2 presents the main ingredients used in statistical inference.
The
key concepts of likelihood, sufficiency, posterior distribution, exponential fam ily, Fisher information and exchangeability are introduced here.
The issue of
16 Introduction parameter elimination leading to marginal, conditional and profile likelihoods is presented here too. A key element of statistical inference for the Bayesian approach is the use of prior distributions. These are separately presented and discussed in Chapter 3. Starting from an entirely subjective specification, we move on to functional form specifications where conjugate priors play a very important role.
Then, non
informative priors are presented and illustrated. Finally, the structuring of a prior distribution in stages with the socalled hierarchical form is presented. Chapter 4 deals with parameter estimation where, intentionally, point and in terval estimation are presented as different responses to the same summarization question, and not as two unrelated procedures. Different methods of estimation (maximum likelihood, method of moments, least squares) are presented. The clas sical results in estimation are shown to numerically coincide with Bayesian results obtained using vague prior distributions in many of the problems considered for the normal observational model. Chapter 5 deals with approximate and computationally intensive methods of inference.
These results are useful when an explicit analytic treatment is not
available. Maximization techniques including NewtonRaphson, Fisher scoring and EM algorithms are presented. Asymptotic theory is presented and includes the delta method and Laplace approximations. Quadrature integration rules are also presented here. Finally, methods based on simulation are presented. They include bootstrap and its Bayesian versions, Monte Carlo integration and MCMC methods. Chapter 6 is about hypothesis testing problems under the frequentist approach and also under the various forms of the Bayesian paradigm. Various test procedures are presented and illustrated for the models with normal observations. Tests based on the asymptotic results of the previous chapter are also presented. Chapter 7 deals with prediction of unknown quantities to be observed. The pre diction analysis is covered from the classical and Bayesian point of view. Linear models are briefly introduced here and provide an interesting example of predic tion. This chapter also includes linear Bayes methods by relating them to prediction in linear models. Chapter 8 deals with linear models. Initially, the frequentist inference for linear models is presented. followed by the Bayesian one.
Generalizations based on
the Bayesian approach are presented leading to hierarchical and dynamic models. Also, a brief introduction to generalized Fnear models is presented.
Exercises § 1,2 I. Consider the equation
P(A n F)
=
P(A I F)P(F) in the light of the
de Finetti loss function setup with the three losses associated with events A
=
and
0, F
r.
=
I,
A
=
I, F
=
I and
F
=
0 and respective probabilities p, q
Show that losses are aJI minimized when p
= r/q.
Exercises 17 §1.3 2. Consider the example of the pregnant lady. (a) Show that by proceeding on Z with the reasoning used for Y leads to the estimation of e by
5/6.
(b) Evaluate the probabilities that Y 2:
5
and Z 2:
5
and show that the
values of these probabilities are respectively given by
3.
0.109 and 0.031.
Consider again the example of the pregnant lady. Repeat the evaluation of the
P(Xr+s+l = l [ (r, s)) assuming now that
(r, s) are (15, 3) using beta priors with parame b) = (I, I) and (a, b) = (5, 5). Compare the results obtained.
(a) the observed values of ters (a,
(b) it is known that her 7th pregnancy will produce twins and the observed value of
(r, s)
is
(5, 1).
§1.4 4. Let
XIY� bin(Y, n:) and let Y � Pois(!.).
(a) Show that
E(X) = E[E(XjY)] = !.n: and that V(X)
(b) Show that 5. Let
=
E [ V(X j Y) ] + V[E(XIY)J = !.n:.
X� Pois(!.Jr) and that Y XIX� Pois(!.(l  n:)].
XIY � N(O, v1) andY� G(a/2, b/2). Obtain the marginal distri' Y[X.
bution of X and the conditional distribution of
6;
X "'"" Apply the result c + CtL, C C'). N( J: to obtain the marginal distribution of any subvector of X.
Show that nonnality is preserved under linear transformations, i.e., if
N(tL, 1:) and Y= c + C X then Y
�
7. Show that if
N(ILXIY• 1:xiY) where ILXIY = /Lx + J:XY 1:y1 (Y /Ly) and J:x 1:xr1:y11:xr.
then XjY�
J:x1r
=
8. Show that if
X , ... , XP are independent standard normal variables then 1 p
L;x?� x� . i=l
9. Show that if X= (X 1o • • • , Xp)' � N(/L, 1:) and Y = (X tL)'J:1 (XtL) then y � x2 p· Hint: Define Z= A(X tL) where the matrix A satisfies A'A= 1:1 and use the result from the previous exercise. I 0. Let A, and B be nonsingular symmetric matrices of orders p and q and ap
x
q matrix. Show that
C
18
Introduction
(a) (A+CBC')1 =A1A 1C(C'A1C+B1)1C'A1 (b) En' o
1 1 1 where D = B  C'A C and E = A c.
'
)
2 Elements of inference
In this chapter, the basic concepts needed for the study of Bayesian apd classical statistics will be described. In the first section, the most commonly used statistical models are presented. They will provide the basis for the presentation of most of the material of this book. Section 2.2 introduces the fundamental concept of likelihood function. A theoretically sound and operationally useful definition of measures of information is also given in this Section. The Bayesian point of view is introduced in Section 2.3. The Bayes' theorem acts as the basic rule in this inferential procedure. The next section deals with the concept of exchangeability. This is a strong and useful concept as will be seen in the following chapter. Other basic concepts, like sufficiency and the exponential family, are presented in Sec tion 2.5. Fina1ly, in Section 2.6, the multiparametric case is presented and the main concepts are revised and extended from both the Bayesian and the classical points of view. Particular attention is given to the problem of parameter elimination in order to make inference with respect to the remaining parameters.
2.1
Common statistical models
Although the nature of statistical applications is only limited by our ability to fonnulate probabilistic models, there are a few models that are more frequently used in statistics. There is a number of reasons for it: first, because they are more commonly found in applications; second, because they are the simplest models that can be entertained in nontrivial applications; finally, because they provide a useful starting point in the process of building up models. The first class of models considered is a random sample from a given distribution. They are followed by the location model, the scale model and the locationscale model. Excellent complementary reading for this topic is the book of Bickel and Doksum ( 1977). The most basic situation of observations is the case of a homogeneous population from a distribution Fe, depending on the unknown quantity 0. Knowledge of the value of e is vital for the understanding and description of this population and we would need to extract infonnation from it to accomplish this task. Typically in this case, a random sample X 1,
•
•
•
,
Xn is drawn from this population and we hope to
20 Elements of inference build strategies to ascertain the value of e from the values of the sample. In this case, the observations X
1
, ... , Xn are independent and identically dis
tributed (iid, in short) with common distribution Fe. Assuming that Fe has density or probability function f, they are probabilistically described through n
p(X1,
. . •
, Xn j8) =
n /(Xi 18).
i=l
Example. Consider a series of measurements made about an unknown quantity(). Unfortunately, measurements are made with imprecise devices which means that there are errors that should be taken into account. These errors are a result_of many
(sma11) contributions and are _more effectively ·described in terms of a probability distribution. This leads to the construction of a model in the form X; I,
. .
.
, n.
=
f)+ ei, i
=
The e;'s represent the measurementeiTors involved. If the experiment is
perfonned with care with measurements being. collected indeP.\'Qd�ntly and using the same procedures, the ei's will form a random sample from the.· distribution of errors
Fe. For the sam� reason, the Xi's will also be iid with joint density n n f(x;l8) = p(X1,.:. ,.x,\8) = /,(Xi 8) i=l i=l
n
where
n
fe is the density of the error distribution.
Definition (Location model). X has a location model if a function f and a quan
tity 9 exist such that the distribution of X given 9 satisfies p(x
In this case, 9 is called a location parameter.
I 9) = f(x 9).
Examples. 1. Normal with known variance
In this case the density is p(xl8) = which is a .function of X  8.
(2Jrcr2)0·5 exp{0.5cr2(x 8)2),
2. Cauchy with known scale parameter The Cauchy distribution is the Student t distribution with I degree of free
dom. In this case, the density is p(xl8) = {rrcr[l + (x 8)cr2]) 1, which
is a function Of X

0, toO.
3. Multivariate normal with known variancecovariance matrix
In this case the density is
p(xl9) = (2Jr)PI211: i1i2 exp{(x 9)':!:1 (x 8)j2), which is a function of
x
8. Note that an iid sample from the N(8, cr2)
distribution is a special case with 9 = 01 and 1: = u2I.
Definition (Scale model). X has a scale model if a function f and a quantity"
exist such that the distribution of X is given by
p(x I cr) =
�� (�)
Likelihoodbased functions 21 In this case,
a is called a scale parameter.
Examples. 1. Exponential with pararneter8
p(xl 8)= 8 exp( ex) is in the fonn at f(xja) withe = crt f(u) = e•.
The density and
2. Normal with known mean()
1 p(xla2) = (2rr)1i2a exp { [(x a1 f(xja).
The density
Definition (Location and scale model). are a function

8)/af/2 J is in the fonn
X has location and scale model if there
f and quantitiese and a such that the distribution of X given (8, a)
satisfies
p(x I e, a)=
� C� ) f
In this case,e is called the locationparameter and
8
a
.
the scale parameter.
Some examples in the locationscale family are the normal and the Cauchy distributions. Once again, the location part of the model can also be multivariate.
2.2
Likelihoodbased functions
Most statistical work is based on functions that are constructed from the proba bilistic description of the observations. In this section, these functions are defined and their relevance to statistical inference is briefly introduced. These functions will be heavily used in later chapters, where their importance will be fully appre ciated. We start with the likelihood function and then present Fisher measures of information and the score function.
2.2.1
Likelihood function
0 is the function that associates the value p(x I 0) to 0. This function will be denoted by 1(0; x). Other common notations are lx (O), 1(0 I x) and 1(0). It is defined as follows The likelihood function of
each
I(·; X): 8+ R+ 0+ 1(0; x)= p(x I 0). The likelihood function associates to each value of(}, the probability of an observed value x for X. Then, the larger the value of l the greater are the chances associated to the event under consideration, using a particular value of 8. fixing the value of
Therefore, by
x and varying 8 we observe the plausibility (or likelihood)
of each value of 8. The concept of likelihood function was discussed by Fisher,
22
I
Elements of inference
It
� �
00
0
t f
[
"' 0
r ' f
\1
£
�
"
!
0
� 0
0
�
'
I
(.
' ' 0.4
0.2
0.0
i 1 i
1.0
0.8 theta
Fig. 2.1 Likelihood function of the example for different values of x. Barnard and Kalbeifhesh among many others. The likelihood function is also of fundamental importance in many theories of statistical inference. Note that even though
JR p(x I 0) dx
Example.
X
�
=
l,
f81(0; x) dO= k
i' l, in generaL
bin(2, B)
p(x I B)= l(B; x)=
G)
G) f
ex ( l
Bx(l B)2X
2; e
E e= (0 , l)
X=
0, l,
B)2 x d8=
G)
B(x+l,3x) =
= 21W
8).
but
fe
l(8;x)dB=


�""'
l.
Note that: l. If
x=
l then
l(B; x =
l)

The value of
8
with highest
8 is 112. 2 then 1(8; X= 2)= 82, the most likely value of e is l. x = 0 then 1(8; x = 0) = (l 8)2, the most likely value is again 0.
likelihood or, in other words, the most likely (or probable) value of
2.
If X =
3. If

These likelihood functions are plotted in Figure
2.1.
The notion of likelihood can also be introduced from a slightly more general perspective. This broader view will be useful in more general observation contexts
Likelihoodbased functions 23 than those considered so far.
These include cases where observation are only
obtained in an incomplete way. Denoting by E an observed event and assuming the probabilistic description of E depends on an unknown quantity fJ, one can define the likelihood function of(} based on the observation E as 1(0; E) ex Pr(EIO) where the symbol ex is to be read as is prop ortional to. If E is of a discrete nature, there is no difference with respect to the previous definition.
Example. Let X 1, . . , X n be a collection of iid random variables with a common .
Bernoulli distribution with success probability (). Let E be any observation of the X 1,
•
•
•
, Xn consisting of x successes. Then, /(e; E)
ex
Pr(Eie) =ex(1ey•x.
For continuous observations, assume that any observed value x is an approxima tion of the real value due to rounding errors. Therefore, the observation x in fact corresponds to t�e observed event E = {x : a .:::: x .:::: a + D.} for given values of a and to.
>
0, which do not depend on
9. In this case, Pr(E19) = F(a +to.) F(a)
where F is the distribution function of the observation. For typical applications, the value of to. is very small and one can approximate F(a +to.) F(a) = p(xiO)!o.. Therefore, 1(0; E)
x; (9) i=l
and
l(9) =
X;, i
=
1, .. .
, n,
n
I:, I; (9).
i=l
The lemma states that the total information obtained from independent observa tions is the sum of the information of the individual observations. This provides
Likelihoodbased functions 25 further intuition about the appropriateness of the Fisher measures as actual sum
maries of information.
Proof. p(X I 0) I 0). Then,
(X;
= n7=1p;(X;
a2Iogp(X
I 0) and therefore logp(X I 0)
1 0)
aoao'
=
_
t
a2logp;(X;
XI0
on both sides, gives
1(0)
=


E
[ t
i=l
�; I O) I o
aoao
azlogp;( X;
E
Taking expectation with
azlogp;(
i=l
t [
I 0)
aoao'
i=l
which proves the result about observed information.
respect to
= L:7=1logp;
I 0)
aoao'
I
o
]
]
n
L, 1;(9).
=
i=l
0
Another very important statistic involved in the study of the likelihood function
is the score function.
Definition.
The score function of X, denoted as U(X; U(X·'
8)
In the case of a parametric vector
vector U(X;
0)
(J
a logp(X
defined as
I 8)
ae
=
=
8), is
(611, . , ()p)T, the score function is also a 0) =a logp(X I O)jae1, i = I, ... , p. . .
with components U., (X;
The score function is very relevant for statistical inference as wiH be shown in
the next chapters. The following lemma shOws an alternative way to obtain the
Fisher information based on the score function.
Lemma.
Under certain regularity conditions, I (8)
In the case of a vector parameter
1(0)
=
Exw[U2(X; 8)].
0, the result becomes
= Ex18[U(X;
O)U' (X; 0)].
Although we shall not go into the technical details of the regularity conditions,
the main reasons for their presence is to ensure that differentiation of the likelihood
can be performed over the entire parameter space and integration and differentiation can be interchanged.
26 Elements of inference Proof.
Using the equality
J
p(X
I
6)dX
=1
and differentiating both sides with
respect to 8, it follows, after interchanging the integration and differentiation operators, that
o
=I
ap(XI 6) a6
dx
=I 1 =I p(X
1
OJ
ap(XI 6)
alog p(XI a9
ao
0)
P
(XI 6) d x
p(XIO) d X.
Therefore the score function has expected value equal to a zero vector. Differenti
ating again with respect to (J 31nd interchanging integration and differentiation we
have
[ =I J I . = l[ ��X ][ ��X r
·
a log p(X
0
ao
alog
1 9)
]O)
ap(X
a9
1
O) '
alog
dX
IO)
+
a2log p(XI 9) a9ao'
p(XIO)dX
P
(X
1
/(9). 0
Bayes' theorem
We have seen that the statistical inference problem can be stated as having an
unknown, unobserved quantity of interest 8 assuming values in a set denoted by
e.
8 can be a scalar, a vector or a matrix. Until now, the only relevant source
of inference was provided by the probabilistic description of the observations. In
this section, we will formalize the use of other sources of information in statistical
inference. This will define the Bayesian approach to inference.
LetH (for history) denote the initial available information about some parameter
of interest. Assume further that this initial information is expressed in probabilistic
terms. It can then be summarized through p(OIH) and, if the information content of H is enough for our inferential purpose, this is all that is needed. In this case the description of our uncertainty about
9
is complete.
Depending upon the relevance of the question we are involved with, H may not
be sufficient and, in this case, it must b� augmented. The main tool used in this
case is experimentation. Assume a vector of random quantities X related to(} can be observed thus providing further information about(}. (If X is not random then
a functional relationship relating it to (} should exist. We can then evaluate the value of
0
and the problem is trivially solved.) Before observing X, we should
know the sampling distribution of X given )?y p(X
1 0,H), where the dependence
=
on fJ, central to our argument, is clearly stated. After observing the value of X, the
amountof information we have about8 has changed fromH toH*
In fact, H* is a subset of H (a refinement on H was performed).
Hn{X
=
If ;
9)dX
The result follows straightforwardly.
2.3
[
I I
x}.
Bayes' theorem 27 Now the information about O is summarized by p(O ing question left is how to pass from
1 x,H) and the only remain p(OI H) to p(OI x, H). From Section 1.4,
one can write
p(OI x ' H)=
p(B,xI H) p(x 18, H)p(OI H) = p(xI H) p(x I H)
where
p(x 1H)=
[ p(x,e Je
1H) de.
The result presented above is known as Bayes' theorem. This theorem was in troduced by the Rev. Thomas Bayes in two papers in after his death, as mentioned in
l'!amett ( 1973).
1763
and
1764,
published
As we can see the function in the
denominator does not depend upon 6 and so, as far as the quantity of interest fJ is concerned, it is just a constant. Therefore, Bayes' theorem can be rewritten in its more usual form
p(OI x)
The dependence on
cx
p(x I
0)p(O).
H is dropped, for simplicity of notation, since it is a common
factor to all the terms. Nevertheless, it should not be forgotten. The above formula is valid for discrete and continuous, scalar, vector and matrix quantities. theorem provides a rule for updating probabilities about leading to
p(8I x).
The
8, starting from p(IJ) and
This is the reason why the above distributions are called prior
and posterior distributions, respectively. To recover the removed constant in the former equation, it is enough to notice that densities must integrate to I and to rewrite it as
p(OI x)= kp(x I O)p(O) where I=
{ p(OI x) dO= k { p(xI O)p(O) dO le Je
and hence
k1 = p(xiH)=
{ p(xI O)p(O)dO fe
= Eo[p(xI
0)].
This is the predictive (or marginal) distribution of X. As before, after removing
the dependence on H, it can be denoted by p(x). This is the expected distribution
of X (under the prior) and it behaves like a prediction, for a given H. So, before observing X it is useful to verify the prior adequacy through the predictions it provides for X. After observing X, it serves to test the model as a whole. An observed value of X with a low predictive probability is an indication that the stated model is not providing good forecasts. This is evidence that something unexpected happened. Either the model must be revised or an aberrant observation occurred.
28 Elements of inference 2.3.1
Prediction
Another relevant aspect that foHows from the ca1cu1ations presented above is that we obtain an automatic way to make predictions for future observations. If we want to predictY, whose probabilistic description is P(Y I
p(y I x) = = = '
9), we have
[ p(y,9 I x) d9 le
fe
p(y 19,x)p(91x)d9
[ p(y I 9)p(9 I x)d9, le
where the last equality follows from the independence between X andY, once
9
is given. This conditional independence assumption is typically present in many statistical problems. Also, it follows from the last equation that
p(ylx) = Eelx[p(y19)]. It is always useful to concentrate on prediction rather than on estimation because the former is verifiable. The reason for the difference is thatY is observable and B is not. This concept can be further explored by reading the books of Aitchison and Dunsmore (1975) and Geisser (1993).
Example. John goes to the doctor claiming some discomfort. The doctOr is led to believe that he may have the disease A. He then takes some standard procedures for this case: he examines John, carefully observes the symptoms and prescribes routine laboratory examinations. Let () be the unknown quantity of interest indicating whether John has disease A or not. The doctor assumes that P(8 = II H) = 0.7.
H in this case contains
the information John gave him and all other relevant knowledge he has obtained from former patients. To improve the evidence about the illness, the doctor asks John to undertake an examination. Examination X is related to () and provides an uncertain result, of the positive/negative type, with the following probability distribution
l
P(X=I I 8 = 0) = 0.40, P(X = I I 8 = I) = 0.95,
positive test without disease positive test with disease.
Suppose that John goes through the exrupination and that its result is X = I. So, for the doctor, the probability that John has the disease is
P(8 =I I X=I) o< 1(8 =I; X= l)P(8 =I) 0(
(0.95)(0.7) = 0.665
and the probability that he does not have the disease is
P(8 = 0 I X=I) o< 1(8 = 0; X= l)P(8 = 0) 0(
(0.40)(0.30) = 0.120.
Bayes' theorem 29 The normalizing constant, such that the total probability adds to so that k(0.665) +
1, is calculated
k(O.I20)= 1 and k = 1/0.785. Consequently P(& =I
I X= 1)= 0.665/0.785 = 0.847
and P(& = The information
0 I X= I)=0.120/0.785 = 0.153.
X = I increases, for the doctor, the probability that John has 0.7 to 0.847. This is not too much since the probability that
the disease A from
the test would give a positive result even if John w�re not ill was reasonably high. So the doctor decides to ask John to undertake another test Y. again of the positive/negative type, where
(
P(Y =I P(Y =
I e = 0) 0.04 I I e = I)= 0.99. =
.
Note that the probability Of this new test yielding a positive result given that John doesn't have the illness is very smal� .. Alth_ough this test might be more expeHsive,
its results are more efficient.
The posterior distribution of
e given X' P(B I X), will be the prior distribution
for theY test. Before observing the result of testY, it is useful to ask ourselves what will the predictive distribution be, that is, what are the values of P(Y = y
1 X=1),
for y =0, l. As we have alreadY seen, in the diScrete case,
= I> Bayesian) follows trivially from Theorem 2.4; 2. (Bayesian ==> classical)
p( O 1 x)=
p(x I O)p(O) p(x)
= f(t, O)p(O),
by hypothesis.
So,p(x I 0)= f(t, O)p(x) which, by the factorization criterion, is equiva
lent to saying that T is a sufficient statistic.
Definition. Suppose that X has density p(x I 0). Then T(X) is an ancillary
statistic for 0 if p(t I 0) = p(t). of
In this case, T does not provide any information for 0 althoug)l.
X, which is related to
8.
sufficiency.
it is.a function
Ancillarity can be understood as an dntonym for
Sufficiency is a baSic concept in classical statistic although it is not so relevant
for the Bayesian approach: On the other hand,from the applied point of view this
is also not a very useful concept SinCe even small perturbations in the model can
imply the loss of sufficiency.
Examples. I. Let X= (Xt .... , 1
1 e)=e.
Xn) be observations with values 0 or l, where P(X; =
n p(xle)=e'(le) r,
witht=
From the factorization criterion it follows that
n
L x;. i=l
T(X)= I:7=t X; is a suffi
cient statistic. In this case, it is also possible to conclude straightforwardly
from the definition of sufficiency and using some combinatorial arguments, that
T(X) is a sufficient statistic since p(x I T(x) = t) = [(;)r1• which
does not depend on e.
2. Let X 1.
Then:
Xz, ..., Xn be iid conditional on 0 with common density p(x; I 0). p(XJ, . .. ,x;, I 0)=
n
n p(x; I 0).
i=l
The order statistics are defined as Yt = Xo) = ntin; X;, Y2 = X(2) = second smallest sample value , .. . , Yn
=
X(n) =max; X;. Since the order
of the terms does not alter the product and to each x; there corresponds a
unique y; (assuming continuity),
n
n
0 p(x; I 0) 0 p(y; I 0). i=l
ex
i=l
Sufficiency and the exponential family 39 Then, with g(x)
I, t
=
=
(y,,
. . . •
factorization criterion holds and T
Yn) and /(1, 8) n7�t p(y; I 8), the (Yt Yn) is a sufficient statistic =
=
• . . . ,
for 8. Note that the dimension ofT depends upon the sample size. In this case no dimensionality reduction was achieved and the definition becomes deprived of its strength. It is also clear that the sample X itself is trivially a sufficient statistic for 8. The application of the sufficiency concept developed above is not necessarily useful. It is only relevant when the dimension of the sufficient statistic is signif icantly smaller than the sample size. An interesting question is how to obtain a sufficient statistic with maximal reduction of the sample data. Such a statistic is known in the literature as a minimal sufficient statistic.
Definition. Let X

p(x I 8). The statisticT(X) is a minimal sufficient statistic
for 0 if it is a sufficient statistic for lJ and a function of every other sufficient statistic for 8. If S(X) is a statistic obtained as a bijective function of a sufficient statisticT(X)
then S is also a sufficient statistic. On the other hand, the minimal sufficient statistic is unique, apart from bijective transformation of itself.
Definition. Two elements x and y of the sample space are information equivalent if
and only if the ratio p(xj8)(p(yiO) or equivalently 1(8; x)(l(O; y) does not depend on 8.
Information equivalence defines an equivalence relation of the elements of the sample space. Therefore, it defines a partition of the sample space. This partition is called a minimal sufficient partition. It can be shown that a sufficient statistic is minimal if and only if the partition it defines on the sample space is minimal sufficient.
Example. Let Xt .... , Xn be iid Poisson variables with mean A and define T(X) 2::7�1 X;. Then, =
p(xiA)
=
).Xi
fl p(x;IA) fl e'"x;! =
i=l
i=l
=
e"'"
T(X)
;... n

ix;!
.
AT(X)T(Y) 01(y; !)/Xi!), which does not depend on T(y). Hence, T(X) is a minimal sufficient statistic for A.
Therefore, p(XIA)/p(yiA)
A if and only if T(x)
n
n
=
=
Another interesting question is whether there are families of distributions ad mitting fixed dimension sufficient statistics for 8. Fortunately, for a large class of distributions, the dimension of the sufficient statisticT is equal to the number of parameters. Maximum summarization is obtained when we have one sufficient statistic for each parameter. Subject to some weak regularity conditions, all dis tributions with the dimension ofT equal to the number of the parameters belong to the exponential family.
40 Elements of inference Definition. The family of distributions with probability (density)function p(x I 8) belongs to the exponential family with r parameters if p(x 1 8) can be written as p(x 19) =a(x)exp
{t
}
Uj(X)j(9)+b(9) ,
x EXC R
and X does not depend on 8. By the factorization criterion, Ut (X), ... , U,(X) are sufficient statistics for 8 (when a single X is observed). For a size n sample of X we have
p( x 19) =
[o J {t [� ] �(x;) exp
}
Uj(X;) j(O) + nb(O)
which belongs to the exponentialfamily too, with a(x) = n7�1 a(x;) and Uj(X) = Uj(X;), j = 1, . ., r. So, T = (TJ, .. , T,) with Tj = Uj(X), j = 1, 2, , r is a sufficient statistic for 8. The exponential family is very rich and includes most of the distribUtions more commonly used in statistics. Among the most important distributions not included in this family we have the unifonn distribution (which has sufficient statistics with dimension not depending on the sample size) and the Student t distribution (which have none). Darmois, Koopman and Pitman have independently shown that among families satisfying some regularity conditions, a sufficient statiStic of fixed dimension will only exist for the exponential family.
I:7�1
.
.
.
.
.
Examples. 1. Bernoulli(@) p(X I@) =@x(l@)1xl ({O, 1}), x
\ C� )
=exp x log
e
}
+log( le) l ({O,I]). x
For a sample x we have, n
p(x I 8) =n ox; (I  8)1�x; lx; ({0, I}) i=l
=exp
{tx;
log
C� ) g
+n log(! 8)} /x({O, !)").
Then, a Bernoulli belongs to the one parameter exponential family with a(x) = /x({O, !)"), b(@) = n log( I @), ¢(8) = log[@/(!  @)] and U(X) = L7=J xj. So, u is a sufficient statistic fore as we have seen before.
Parameter elimination 41
2.
Poisson().)
eA;.x
P(x I).)= 1Ix([O, X.
)
1, ... ) ,
which in the exponential form is 1
p(x I>.) = ! exp{>. + x log A]lx( {O, 1, . )). x . .
For a sample x we have,
p(x I).)=
n?:,
x ;!
exp
{�X;
log). n).
}
lx({O,
1, . . . ]").
So, the Poisson belongs to the one parameter exponential family with a(x)=lx({O, 1, . . ]")/ n7�1 x;!, b(>.) n>., if>_(>.) = log>. and_[.!()() : = L7=I X;. Then U is sufficient for A. 3. Normal(f', a 2 ) .
=
p(x If', a2) =.
1
r;c
.
v2na
exp
1
=  exp v'Ziia
=
1
·
(  ) ( J!:_x x2 .!!!__) a (x .
1')2 2a2
1  _
1._ 2
�'
v'2Ji exp a2xla2x
(
2 2
2a2
cr2
I''

1
2 l 2 21oga a
)
.
For a sample x it follows,
p(x If', a')=
1 (2 rr)•i2 x·exp ·
I'
n
1
n
2 n
xi { 2L a i=I xi2a2L i=l
(
2 I' z+Ioga2
2 a

)}
.
Then, the normal distribution is a member of the exponential family with a bidimensional parameter 8 = (f', a2), a(x) = (2rr)•i2, b(O) = (n/2 ) [(1'2/a2)+loga2],ifJI(8) =!'2/a2, in the (total)likelihood by some Particular value. Oft�n, this is taken as the value that maximizes the likelihood. Denoting this value by (8), because it can depend on 8, we get the relative or profile likelihood for 8 as 1(8, ¢ is given by the lefthand side of the equation above and the righthand side terms can also be written in likelihood tenns as 1(8, ; t, u) = 1(8, ; t)/(8, ; u I t). The first term on the righthand side is called the marginal likelihood and the second, the conditional like.lihood. These forms of likelihood are useful when some of the parametric components can be eliminated. For example, if T is such that 1(8, ; t) = 1(8; t) only this term is used to make inferences about 8. For conditional likelihood, this form is related to sufficient statistics because if T is sufficient for , with 8 fixed, then/(8, ; u It)= 1(8; u It). Againonly this term is used to make inferences about 0. The question is how much information is lost when ignoring the other part of the likelihood.
.
Example. Let X= (X 1, .. , Xn) be a random sample from a N(8, a2) and define ¢ a2. The parameter vector (8, )is unknown and we assume that the main interest lies in the mean of the population. The precision ¢ is essentially a nuisance parameter that we would like to eliminate from the analysis. Suppose that the prior distribution is such that noaJ¢ x;0 or equiva1ently ¢ G(no/2, noaJ/2) and 1> is independent of 8 a priori. In these conditions we have =
"'

p ( 18) On the other hand,
=
p(¢) ex q,•o/Zl exp
{

noa2 /¢
}
.
44 Elements of inference but
n
2)x1 8)2
n
L [ (x1
=

i=l
i=l
x)+ (x 8)]2
n
I: 0, fJ> 0
(a) Obtain the lik(;lihood function, the score function and the observed and expected Fisher information matrix for the pair of parameters (a,
{3).
(b) The Weibull distribution is sometimes parametrized in terms of a and
B
=
1/fJ". Repeat item (a) for the pair of parameters (a, B).
46
Elements of inference
§ 2.3 5. Return to the example about John's illness and consider the same distribu tions. (a) Which test result makes the doctor more certain about John's illness? Why? (b) The test X is applied and provides the result X
=
I. Suppose the
doctor is not satisfied with the available evidence and decides to ask for another replication of test X and again the result is X
=
1. What
is now the probability that John has disease A? (c) What is the minimum number of repetitions of the test X which allows the doctor to ensure that John has the disease with 99.9% PfObability. What are the results of these replications that guarantee this?
6. Suppose that X I e

N(B, l )(for example, X is a measurement of a physical
constant() made with an instrument with variance 1). The prior distribution forB elicited by the scientist A corresponds to a N(5, 1) distribution and the scientist B elicits a N(l5, I) distribution. The value X= 6 was observed. (a) What prior fits the data better? (b) What kind of comparison can be done between the two scientists? 7. Classify the following assertions as TRUE or FALSE, briefly justifying your
answer. (a) The posterior distribution is always more precise than the prior because it is based on more infonnation. (b) When X2 is observed after X1, the prior distribution before observing X2 has to be necessarily the posterior distribution after observing X I·
(c) The predictive density is the prior expected value of the sampling distribution. (d) The smaller the prior information, the bigger the influence of the sam ple in the posterior distribution. 8. A test to verify if a driver is driving in a drunken state has 0.8 chance of being correct, that is, to provide a positive result when in fact the driver has a high level of alcohol in his/her blood or negative result when it is below the acceptable limit. A second test is only applied to the suspected cases. This never fails if the driver is not drunk, but has only a 10% chance of error with drunk drivers. If25% of all the drivers stopped by the police are above the limit, calculate:
(a) the proportion of drivers stopped that have to be submitted to a second test; (b) the posterior probability that this driver really has the high level of alcohol in his blood informed by the two tests; (c) the proportion of drivers that will be submitted only to the first test.
Exercises
47
9. (DeGroot, 1970, p. 152) The random variables
X1, ... ,Xk are such that  1 of them have probability function h and one has probability function g. Xj will have the probability function g with probability aj• j I, . . , k, where 0, Vj and I:J�1 Uj 1. What is the probability that X1 has
k
=
.
=
probability function g given that: (a) X
1
=
x was observed?
(b) X; =x,i f 1 was observed? 10. Let X I 8, J1. � N( 8, cr2), cr2 known and 8 I J1. � N(Ji., r2), r2 known and J1. � N(O, 1). Obtain the following distributions: (a) (8 I x. JL); (b) (Jl. I x); (c) (8 I x).
11. Let (X18) N(8, I) be observed. Suppose that your prior is such that 8 is N(Jl., 1) or N(JL, 1) with equal probabilities. Write the prior distribution and find the posterior after observing X =x. Show that �
Jl.'
and draw a graph of
= E(8 I x) =
:: 2
+
1!:1exp(JJ.x) 21 +exp(!lx)
tl' as a function of x.
12. The standard Cauchy density function is p(xl8) (1/rr){ 1/[1 + (x 8)2]} and is similar to N(8, 1) and can be used in its place in many applications. Find the modal equation (the first order condition to the maximum) of the posterior supposing that the prior is p(8)= 1/rr ( l + 82). =
(a) Solve it for x =0 and x = 3.
(b) Compare with the results obtained assuming that (x18) � N(8,I)and 8 � N(O, 1). 13. Assume that. an observation vector X has multivariate normal.distribution, introduced in Chapter 1, with mean vector JL and variancecovariance matrix L. Assuming that L is known and the prior distribution is J.t,...., N(f.Lo, Bo) obtain the posterior distribution for fl.
§ 2.4
14. Let X = (X1,. . , X,) be an exchangeable sample of 01 observations. Show that .
(a) E[X;] = E[Xj]. Vi,j =I,... , n; (b) V[X;]=V[Xj],Vi,j=l,. . ,n; (c) Cov(X;,Xj)= Cov(X, X,), Vi,j, k, I=I,... ,n. .
15. Let X = (X1, ,X,) be an exchangeable sample of 01 observations and T = 2::7�1 X;. Show that • • •
(a) P(T=t)=Jd(�)8'(18)"1p(8)d8,
t=l,... ,n.
48 Elements of inference (b) E(T)= nE(B). Hint: in (b), use the definition of E(T) and exchange the summation and integration signs. 16. Let 01, . . . ,Bk be the probability that patients
I 1, ... , I, have the disease
B.
After summarizing all the available information the doctor concludes that (a) The patients can be divided in two groups, the first containing the patients ft, . . .
, Ij. j
... ,Bk). If instead of (c), there was information relating the two § 2.5
groups, What modifications. w�uld this imply in the prior for 0?
17. Let X= (X 1,
. . . •
Xn) be a random sample from U(B1. Bz). that is, I
p(xiBI,Bz)= n • 02 VJ
B1:sx:sBz.
Let T(X) = (X(I)• XcnJ), obtain its joint distribution and show that it is a sufficient statistic for 0
=
(B1,Bz).
18. Let X be a random sample from P(X I 0). Show that if T = T(X) is a sufficient statistic for 0 and S(X) is a 1to1 function ofT then S(X) is also a sufficient statistic for(}.
19. LetX1
• . . .
,Xn be a random sample fromP(X I
B1,Bz). Show that if T1
is sufficient for Bt when 82 is known and T2 is sufficient for 8z when Bt is
known, then T= (T1, Tz) is sufficient for B= (B1,Bz).
20. Verify whether the following distributions belong to the exponential family. If so, determine the functions
a,
b,
u
and¢.
(a) bin(n, B),n known; (b) exp(B); (c)
G(a, {J); (a, {J); (e) N(p,,1;), 1; known.
(d) beta
21. Which of the following distribution families are members of the exponential family? Obtain the minimal sufficient statistic for those belonging to the exponential family. (a) p(x
I B)= 1/9,X
E
{0.1 + e, . . . '0.9 + B}; '
(b) the family of N(B,B2) distributions;
(c) the family of N(B, B) distributions, with B
>
(d) p(x I B)= 2( x + B)/(1 + 2B),X E (0,1), e
0; >
0;
Exercises (e) the distribution family of X I X cp 0 where (f) f(x I 8) =8/(1 +x)1+0, x E R+;
49
X� bin(n,8);
(g) f(x 18) =8xlog8/(8]),x E (0, I); (h) f(x 18)=(1/2) exp(lx81),x E R.
2
22. Let Xt, .. ., X, be a random sample from N(!L, a2),with a unknown. Show, using the classical definition, that the sample mean X is a sufficient statistic for J.L. Hint: It is enough to show that il· Why? 23. Let
E (X I X) and V (X I X) is not a function of
(Xt, X2, X3) be a vector with probability function n.I
3
n P;
3 ni=lx;li=l
X;
,
Xj 2: 0,
where p1 =82, pz= 28(1 8), P3 =(I 8)2 and 0 :5 e :5 I. (a) Verify whether this distribution belongs to the exponential family with k parameters. If this is true,what is the value of k? (b) Obtain the minimal sufficient statistic for(}. 24. Using the same notation adopted for the one parameter exponential family, (a) show that
E[U(X )]=
b'(8) rf/(8)

and
V[U(X)] =
b'(8)¢/'(8) '(8)b"(8) ['(8)]3
Hint: From the relationship J p(x I 8) dx = 1, differentiate both sides with respect to e. (b) Verify that the result in (a) is correct for the case where by direct evaluation of E[U(X)] and V[U(X)].
X�
exp(8)
25. Show that information equivalence defines an equivalence relation of the elements of the sample space. 26. Let Xt. X2, X3 be iid Bernoulli variables with success probability 8 and define T = T(X) = L;j= X;, Tt = Xt and T2 = (T, Tt)· Note that the � sample space isS= {0, I} . (a) Obtain the partitions induced by T, Tt andT2. (b) Show thatTz is a sufficient statistic. (c) Prove that T is a minimal sufficient statistic for 8 but T2 isn't by showing that T induces a minimal sufficient partition on S but T2 does not. 27. Consider a sample X= (X 1, ... , X,) from a common density p(x 18) and letT be the vector of order statistics from the sample.
50
Elements of inference (a) Prove that the sample X is always sufficient for 8 (b) Obtain the factor g(x) in the factorization criterion for the sufficient statistic T.
28. Consider a sample X= (XJ,...,Xn) from a uniform distribution on the interval [Bt. Bz], Bt < Bz, so that 8 = (Bt, Bz ). (a) Show that this distribution does not belong to the exponential family. (b) Obtain a sufficient statistics of fixed size. (c) Specialize the results above for the cases that B1 is known and 82 is known.
29. Consider a sample X= (Xt,...,Xn) from a t,(/L, cr2), and 8= (v, /L, cr2). (a) Show that this distribution does not belong to the exponential family. (b) Show that it is not possible to ob�ain a sufficient stati�tics for 0 of fixed size. (c) Show th:it the results above are retained even for the cases when some of the components of 8 are know�.
§ 2.6
30. Let
X mld Y be independent random variables Poisson distributed with parameters () ahd rp, respectively, and suppose that the prior distribution is p(B, ¢) = p(B)p() ex k. Let 1{f = B/(0 +) and�= B + be a parametric transformation of (B, ). (a) Obtain the prior for (1/f, $).
(b) Show thatl{f I x, y are independent.
�
beta(x+ I, y+I) and� I x, y
�
G(x+y+2, I)
(c) Show that the conditional distribution X given X+ Y depends only on l{f, that is p(x I x + y ,l{f,�) = p(x I x + y,l{f) and that the distribution of X+ Y depends only on�. (d) Show that X+ Y is a sufficient statistic for�, X is a sufficient statistic for 1/f, given X+ Y, and that (X,X+ Y) is a sufficient statistic for (1/f,�). (e) Obtain the marginal likelihoods of 1{f and�.
(f) To make an inference about � a statistician decides to use the fact presented in item (d). Show that the posterior .is identical to that obtained in (b). Does it mean that X+ Y does not contain information about l{f?
31. Suppose that X has density f(x I 8) where 8 = (BJ,/h,BJ) and the prior forB is built up as p(8)= g(BJ,82 I B3)h(B3) where g and hare densities. Obtain the marginal likelihood f(x I Bz,83) as a function of f,g and h. 32. Let X= (Xt,Xz) where Xt = (XtJ,... ,XJm) and Xz = (XzJ,... , Xzn) are samples from the exp(Bt) and exp(/h) distributions respectively. Suppose that independent G(a;,b;) priors are assumed, i= I, 2 and define 1{f= Bt /Bz.
Exercises 51 (a) Obtain the distribution of (BI> B2) given
X= x.
(b) Repeat item (a), assuming now that a,, b,
>
0, i = I,
2.
(c) Using the posterior obtained in item (b), show that XJ
:::1/J I
x
X=
X2 where
I:XIj  =  XJ
�
F(2m, 2n)
Xz=
and
m
Hint: complete the transformation with 33. Let
(Xt, X2, X3)
rameter 8
=
t/11
2· I: X
1 11
= 82.
be a random vector with trinomial distribution with pa
(Bt, B2,
B3)
where
e, = I
 Bt  B2 and assume that the prior
for 8 is constant. (a) Define A = Bt I (Bt + 82) and
1/J =
(b) Obtain the marginal likelihood of (c) Show that
Xt + Xz is
Bt + B2 and obtain their priors.
1/J.
a sufficient statistic for
1/J.
34. A machine emits particles following a Poisson process with mean intensity of A particles per unit time. Each particle generates a N(B,
I)
signal. A signal
detector registers the particles. Unfortunately, the detector only registers the positive signals (making it impossible to observe the number
n
of emitted
particles). (a) Obtain the dis tribution of
kin
where
k
is the number of particles reg
istered. (b) Show that the likelihood l(B, A) based on the observation of just one registered signal
(k = I)
>
assuming the value XJ
0 during a unit
interval is given by
¢(xt B)A(B)exp{A(B)) where 4> and are the density and the cumulative distribution function of the standard normal. Hint: Obtain the joint distribution of
(x,, k, n)
and eliminate
11
by
integration. (c) Obtain the profile likelihood of e, that is, l(B, .i.(B)) where .i.(B) max imizes the likelihood of), supposing e known. (d) Supposing that the prior is p(B, of e.
!..) 0< k, obtain the marginal likelihood
3 Prior distribution
In this chapter, different specification forms of the prior distribution will be dis� cussed. Apart from the interpretation of probability, this is the only novelty intro duced by the Bayesian analysis, relative to the frequentist approach. It can be seen as an element implied from exchangeability by de Finetti's representation theorem. It is determined in a subjective way, although it is not forbidden to use past experi mental data to set it. The only requirement is that this distribution should r:epresent the knowledge about e before observing the results of the new experiment. In this chapter, alternative forms of assessing the prior distribution will be discussed. ·In.· Section 3.1 entirely subjective methods for direct assessment of the prior will be presented. An indirect approach, via functional forms, is discussed in Section 3.2. The parameters of those functional forms, known as hyperparameters, rilust be specified in correspondence with the subjective information available. The conj�
gate distribution will be. introduced in Section 3.3 and the most common fa·milies will be presented in Sectiori 3.4. The concept of reference prior and different forms of building up noninformative priors will be presented in Section 3.5. Finally,
hierarchical prior specification will be discussed in Section 3.6.
3.1
Entirely subjective specification
Let 8 be an unknown quantity and consider its possible values. If it is discrete, a prior probability for each possible value of e can be evaluated directly. Also one may use some auxiliary tools, like lotteries or roulettes, as described in Chapter l. De Finetti ( 1974) characterizes subjective probability through the consideration of betting and scoring rules. The continUous case is slightly more complicated. Some suggestions are: 1. The histogram approach: first, the range of values of 8 is divided into inter vals, and prior probabilities for e belonging to each interval are specified, as in the discrete case. Hence, a histogram for e is built up and a smooth curve can be fitted to obtain the prior density of(}. Note that the number of intervals involVed is arbitrarily chosen. Although the probability in the tails of the prior distribution are often very small, they can influence the subse quent inference. This is a relevant aspect in prior elicitation that deserves
54
Prior distribution
, ...
0
2
4
6
8
10
12
theta
Fig. 3.1 Histogram representing (subjective) probabilities of the intervals h, h, h /4, Is and lo with a fitted density. some caution. Figure 3.1 shows one such elicitation exercise for a positive quantity e.
2. The distribution function approach: First, let us define percentiles. Za is the IOOa% percentile( quantile) of X if P(X : 1 x)
0(
0(
p(x 1
e,
)p(B, ¢)
q,lln+no+ 1)/2]1 x
exp
I
"'

l
)
 8)2] . [noa02+ ns 2+co(B JLo)2 + n(x
It is not difficult to show that
c0(8 JLo)2 +n(B X)2 =(co+n)(8 JL1l2 + � (JLox)2 co+n where
(e, 4>)
J LI = (coJL +nx)f(co+ n).
Thus it follows that the posterior density for
is proportional to
¢ x
[(n+no+l)/2]1 exp
}
con 2 4> [noa02 + ns +  (JLo x)2 +(co+ n)(B JLJ)2 ] . 2 co+n
I
Therefore, the joint posterior for
(JLJ, CJ, n1, a f)
(B, ¢1 I x) is normalgamma with parameters
given by
JLJ=
CQflO+nX co+n
Noninformative priors
65
ct =co+n n1 =no+n
nw12
=
con 2 (JLox) . nocr02 +ns2 + co +n 
Prior and posterior distributions are members of the same family. So, the normal gamma family is conjugate with respect to the nonnal sampling model when e are both unknown. Table 3.1 ·summarizes the distributions involved in the
and
a2
Bayesian analysis of the normal models with unknown mean and variance.
Table 3.1 Summary of the distributions Prior
N(JLo, (co¢)1) noaJ¢ "'"' x;o tn0(JLo, crJ I co) [nocrJ + co(8 �.
th 's are supposed exchangeable as their subscripts are irrelevant in
terms of this prior. Since the distribution of A is indePendent of the first stage, it can be stated as:
I. Concentrated: p(l. 2. Discrete: p(l.
=
>o)
=
Aj)
=
=
I.
pj.j
=
l, . . . ,k, with 'EjPj
=
I . In this case
the distribution of e wW be a finite mixture .