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Bayesian Methods for Function Estimation

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hs25 v.2005/04/21 Prn:19/05/2005; 15:11 F:hs25013.tex; VTEX/VJ p. 1 aid: 25013 pii: S0169-7161(05)25013-7 docsubty: REV Handbook of Statistics, Vol. 25 ISSN: 0169-7161 13 © 2005 Elsevier B.V. All rights reserved. 1 1 DOI 10.1016/S0169-7161(05)25013-7 2 2 3 3 4 4 5 5 6 Bayesian Methods for Function Estimation 6 7 7 8 8 9 9 10 Nidhan Choudhuri, Subhashis Ghosal and Anindya Roy 10 11 11 12 12 Abstract 13 13 14 14 15 Keywords: consistency; convergence rate; Dirichlet process; density estimation; 15 16 Markov chain Monte Carlo; posterior distribution; regression function; spectral den- 16 17 sity; transition density 17 18 18 19 19 20 20 21 1. Introduction 21 22 22 23 Nonparametric and semiparametric statistical models are increasingly replacing para- 23 24 metric models, for the latter’s lack of sufficient flexibility to address a wide vari- 24 25 ety of data. A nonparametric or semiparametric model involves at least one infinite- 25 26 dimensional parameter, usually a function, and hence may also be referred to as an 26 27 infinite-dimensional model. Functions of common interest, among many others, include 27 28 the cumulative distribution function, density function, regression function, hazard rate, 28 29 transition density of a Markov process, and spectral density of a time series. While fre- 29 30 quentist methods for nonparametric estimation have been flourishing for many of these 30 31 problems, nonparametric Bayesian estimation methods had been relatively less devel- 31 32 oped. 32 33 Besides philosophical reasons, there are some practical advantages of the Bayesian 33 34 approach. On the one hand, the Bayesian approach allows one to reflect ones prior be- 34 35 liefs into the analysis. On the other hand, the Bayesian approach is straightforward in 35 36 principle where inference is based on the posterior distribution only. Subjective elici- 36 37 tation of priors is relatively simple in a parametric framework, and in the absence of 37 38 any concrete knowledge, there are many default mechanisms for prior specification. 38 39 However, the recent popularity of Bayesian analysis comes from the availability of 39 40 various Markov chain Monte Carlo (MCMC) algorithms that make the computation 40 41 feasible with today’s computers in almost every parametric problem. Prediction, which 41 42 is sometimes the primary objective of a statistical analysis, is solved most naturally if 42 43 one follows the Bayesian approach. Many non-Bayesian methods, including the max- 43 44 imum likelihood estimator (MLE), can have very unnatural behavior (such as staying 44 45 on the boundary with high probability) when the parameter space is restricted, while 45 377 hs25 v.2005/04/21 Prn:19/05/2005; 15:11 F:hs25013.tex; VTEX/VJ p. 2 aid: 25013 pii: S0169-7161(05)25013-7 docsubty: REV 378 N. Choudhuri, S. Ghosal and A. Roy 1 a Bayesian estimator does not suffer from this drawback. Besides, the optimality of a 1 2 parametric Bayesian procedure is often justified through large sample as well as finite 2 3 sample admissibility properties. 3 4 The difficulties for a Bayesian analysis in a nonparametric framework is threefold. 4 5 First, a subjective elicitation of a prior is not possible due to the vastness of the para- 5 6 meter space and the construction of a default prior becomes difficult mainly due to the 6 7 absence of the Lebesgue measure. Secondly, common MCMC techniques do not di- 7 8 rectly apply as the parameter space is infinite-dimensional. Sampling from the posterior 8 9 distribution often requires innovative MCMC algorithms that depend on the problem at 9 10 hand as well as the prior given on the functional parameter. Some of these techniques 10 11 include the introduction of latent variables, data augmentation and reparametrization of 11 12 the parameter space. Thus, the problem of prior elicitation cannot be separated from the 12 13 computational issues. 13 14 When a statistical method is developed, particular attention should be given to the 14 15 quality of the corresponding solution. Of the many different criteria, asymptotic con- 15 16 sistency and rate of convergence are perhaps among the least disputed. Consistency 16 17 may be thought of as a validation of the method used by the Bayesian. Consider an 17 18 18 imaginary experiment where an experimenter generates observations from a given sto- 19 19 chastic model with some value of the parameter and presents the data to a Bayesian 20 20 without revealing the true value of the parameter. If enough information is provided in 21 21 the form of a large number of observations, the Bayesian’s assessment of the unknown 22 22 parameter should be close to the true value of it. Another reason to study consistency is 23 23 its relationship with robustness with respect to the choice of the prior. Due to the lack 24 24 of complete faith in the prior, we should require that at least eventually, the data over- 25 25 26 rides the prior opinion. Alternatively two Bayesians, with two different priors, presented 26 27 with the same data eventually must agree. This large sample “merging of opinions” 27 28 is equivalent to consistency (Blackwell and Dubins, 1962; Diaconis and Freedman, 28 29 1986a, 1986b; Ghosh et al., 1994). For virtually all finite-dimensional problems, the 29 30 posterior distribution is consistent (Ibragimov and Has’minskii, 1981; Le Cam, 1986; 30 31 Ghosal et al., 1995) if the prior does not rule out the true value. This is roughly a 31 32 consequence of the fact that the likelihood is highly peaked near the true value of the 32 33 parameter if the sample size is large. However, for infinite-dimensional problems, such 33 34 a conclusion is false (Freedman, 1963; Diaconis and Freedman, 1986a, 1986b; Doss, 34 35 1985a, 1985b; Kim and Lee, 2001). Thus posterior consistency must be verified before 35 36 using a prior. 36 37 In this chapter, we review Bayesian methods for some important curve estimation 37 38 problems. There are several good reviews available in the literature such as Hjort (1996, 38 39 2003), Wasserman (1998), Ghosal et al. (1999a), the monograph of Ghosh and Ra- 39 40 mamoorthi (2003) and several chapters in this volume. We omit many details which 40 41 may be found from these sources. We focus on three different aspects of the problem: 41 42 prior specification, computation and asymptotic properties of the posterior distribution. 42 43 In Section 2, we describe various priors on infinite-dimensional spaces. General results 43 44 on posterior consistency and rate of convergence are reviewed in Section 3. Specific 44 45 curve estimation problems are addressed in the subsequent sections. 45 hs25 v.2005/04/21 Prn:19/05/2005; 15:11 F:hs25013.tex; VTEX/VJ p. 3 aid: 25013 pii: S0169-7161(05)25013-7 docsubty: REV Bayesian methods for function estimation 379 1 2. Priors on infinite-dimensional spaces 1 2 2 3 A well accepted criterion for the choice of a nonparametric prior is that the prior has 3 4 a large or full topological support. Intuitively, such a prior can reach every corner of 4 5 the parameter space and thus can be expected to have a consistent posterior. More flex- 5 6 ible models have higher complexity and hence the process of prior elicitation becomes 6 7 more complex. Priors are usually constructed from the consideration of mathematical 7 8 tractability, feasibility of computation, and good large sample behavior. The form of the 8 9 prior is chosen according to some default mechanism while the key hyper-parameters 9 10 are chosen to reflect any prior beliefs. A prior on a function space may be thought of 10 11 as a stochastic process taking values in the given function space. Thus, a prior may be 11 12 specified by describing a sampling scheme that generate random function with desired 12 13 properties or they can be specified by describing the finite-dimensional laws. An advan- 13 14 tage of the first approach is that the existence of the prior measure is automatic, while 14 15 for the latter, the nontrivial proposition of existence needs to be established. Often the 15 16 function space is approximated by a sequence of sieves in such a way that it is easier to 16 17 put a prior on these sieves. A prior on the entire space is then described by letting the 17 18 index of the sieve vary with the sample size, or by putting a further prior on the index 18 19 thus leading to a hierarchical mixture prior. Here we describe some general methods of 19 20 prior construction on function spaces. 20 21 21 22 2.1. Dirichlet process 22 23 Dirichlet processes were introduced by Ferguson (1973) as prior distributions on the 23 24 space of probability measures on a given measurable space (X, B).LetM>0 and G be 24 25 a probability measure on (X, B). A Dirichlet process on (X, B) with parameters (M, G) 25 26 is a random probability measure P which assigns a number P(B)to every B ∈ B such 26 27 that 27 28 28 [ ] 29 (i) P(B)is a measurable 0, 1 -valued random variable; 29 B 30 (ii) each realization of P is a probability measure on (X, ); 30 { } 31 (iii) for each measurable finite partition B1,...,Bk of X, the joint distribution of 31 32 the vector (P (B1),...,P(Bk)) on the k-dimensional unit simplex has Dirichlet 32 (k; MG(B ), .
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