Results 1  10
of
92
Convergence rates of posterior distributions
 Ann. Statist
, 2000
"... We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinitedimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, logspline models, D ..."
Abstract

Cited by 106 (14 self)
 Add to MetaCart
(Show Context)
We consider the asymptotic behavior of posterior distributions and Bayes estimators for infinitedimensional statistical models. We give general results on the rate of convergence of the posterior measure. These are applied to several examples, including priors on finite sieves, logspline models, Dirichlet processes and interval censoring. 1. Introduction. Suppose
Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities
 Ann. Statist
, 2001
"... We study the rates of convergence of the maximum likelihood estimator (MLE) and posterior distribution in density estimation problems, where the densities are location or locationscale mixtures of normal distributions with the scale parameter lying between two positive numbers. The true density is ..."
Abstract

Cited by 62 (10 self)
 Add to MetaCart
We study the rates of convergence of the maximum likelihood estimator (MLE) and posterior distribution in density estimation problems, where the densities are location or locationscale mixtures of normal distributions with the scale parameter lying between two positive numbers. The true density is also assumed to lie in this class with the true mixing distribution either compactly supported or having subGaussian tails. We obtain bounds for Hellinger bracketing entropies for this class, and from these bounds, we deduce the convergence rates of (sieve) MLEs in Hellinger distance. The rate turns out to be �log n � κ / √ n, where κ ≥ 1 is a constant that depends on the type of mixtures and the choice of the sieve. Next, we consider a Dirichlet mixture of normals as a prior on the unknown density. We estimate the prior probability of a certain KullbackLeibler type neighborhood and then invoke a general theorem that computes the posterior convergence rate in terms the growth rate of the Hellinger entropy and the concentration rate of the prior. The posterior distribution is also seen to converge at the rate �log n � κ / √ n in, where κ now depends on the tail behavior of the base measure of the Dirichlet process. 1. Introduction. A
Convergence rates of posterior distributions for noniid observations
 Ann. Statist
, 2007
"... We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor identically distributed. We give general results on the rate of convergence of the posterior measure relative to distances derived from a testing ..."
Abstract

Cited by 57 (4 self)
 Add to MetaCart
(Show Context)
We consider the asymptotic behavior of posterior distributions and Bayes estimators based on observations which are required to be neither independent nor identically distributed. We give general results on the rate of convergence of the posterior measure relative to distances derived from a testing criterion. We then specialize our results to independent, nonidentically distributed observations, Markov processes, stationary Gaussian time series and the white noise model. We apply our general results to several examples of infinitedimensional statistical models including nonparametric regression with normal errors, binary regression, Poisson regression, an interval censoring model, Whittle estimation of the spectral density of a time series and a nonlinear autoregressive model.: θ ∈ Θ) be a sequence of statistical experiments with observations X (n), where the parameter set Θ is arbitrary and n is an indexing parameter, usually the sample size. We put a prior distribution Πn on θ ∈ Θ and study the rate of convergence of the posterior
Dirichlet Process Mixtures of Generalized Linear Models
"... We propose Dirichlet Process mixtures of Generalized Linear Models (DPGLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models a response variable locally by a generalized linear model. We give conditions for the existence and asymptotic unbiasedne ..."
Abstract

Cited by 32 (3 self)
 Add to MetaCart
(Show Context)
We propose Dirichlet Process mixtures of Generalized Linear Models (DPGLMs), a new method of nonparametric regression that accommodates continuous and categorical inputs, models a response variable locally by a generalized linear model. We give conditions for the existence and asymptotic unbiasedness of the DPGLM regression mean function estimate; we then give a practical example for when those conditions hold. We evaluate DPGLM on several data sets, comparing it to modern methods of nonparametric regression including regression trees and Gaussian processes. 1
On rates of convergence for posterior distributions in infinitedimensional
 Ann. Statist
, 2007
"... This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. In particular, we improve on current rates of convergence for models including the mixture of Dirichlet process ..."
Abstract

Cited by 27 (2 self)
 Add to MetaCart
(Show Context)
This paper introduces a new approach to the study of rates of convergence for posterior distributions. It is a natural extension of a recent approach to the study of Bayesian consistency. In particular, we improve on current rates of convergence for models including the mixture of Dirichlet process model and the random Bernstein polynomial model. 1. Introduction. Recently
Misspecification in infinitedimensional Bayesian statistics
 Annals of Statistics
, 2006
"... We consider the asymptotic behavior of posterior distributions if the model is misspecified. Given a prior distribution and a random sample from a distribution P0, which may not be in the support of the prior, we show that the posterior concentrates its mass near the points in the support of the pri ..."
Abstract

Cited by 21 (0 self)
 Add to MetaCart
(Show Context)
We consider the asymptotic behavior of posterior distributions if the model is misspecified. Given a prior distribution and a random sample from a distribution P0, which may not be in the support of the prior, we show that the posterior concentrates its mass near the points in the support of the prior that minimize the Kullback–Leibler divergence with respect to P0. An entropy condition and a priormass condition determine the rate of convergence. The method is applied to several examples, with special interest for infinitedimensional models. These include Gaussian mixtures, nonparametric regression and parametric models.
DYNAMICS OF BAYESIAN UPDATING WITH DEPENDENT DATA AND MISSPECIFIED MODELS
, 2009
"... Recent work on the convergence of posterior distributions under Bayesian updating has established conditions under which the posterior will concentrate on the truth, if the latter has a perfect representation within the support of the prior, and under various dynamical assumptions, such as the data ..."
Abstract

Cited by 21 (3 self)
 Add to MetaCart
(Show Context)
Recent work on the convergence of posterior distributions under Bayesian updating has established conditions under which the posterior will concentrate on the truth, if the latter has a perfect representation within the support of the prior, and under various dynamical assumptions, such as the data being independent and identically distributed or Markovian. Here I establish sufficient conditions for the convergence of the posterior distribution in nonparametric problems even when all of the hypotheses are wrong, and the datagenerating process has a complicated dependence structure. The main dynamical assumption is the generalized asymptotic equipartition (or “ShannonMcMillanBreiman”) property of information theory. I derive a kind of large deviations principle for the posterior measure, and discuss the advantages of predicting using a combination of models known to be wrong. An appendix sketches connections between the present results and the “replicator dynamics” of evolutionary theory.
Bernstein Von Mises Theorem for linear functionals of the density
, 908
"... In this paper, we study the asymptotic posterior distribution of linear functionals of the density. In particular, we give general conditions to obtain a semiparametric version of the BernsteinVon Mises theorem. We then apply this general result to nonparametric priors based on infinite dimensional ..."
Abstract

Cited by 20 (3 self)
 Add to MetaCart
In this paper, we study the asymptotic posterior distribution of linear functionals of the density. In particular, we give general conditions to obtain a semiparametric version of the BernsteinVon Mises theorem. We then apply this general result to nonparametric priors based on infinite dimensional exponential families. As a byproduct, we also derive adaptive nonparametric rates of concentration of the posterior distributions under these families of priors on the class of Sobolev and Besov spaces.
Spline adaptation in extended linear models
 Statistical Science
, 2002
"... Abstract. In many statistical applications, nonparametric modeling can provide insight into the features of a dataset that are not obtainable by other means. One successful approach involves the use of (univariate or multivariate) spline spaces. As a class, these methods have inherited much from cla ..."
Abstract

Cited by 19 (2 self)
 Add to MetaCart
Abstract. In many statistical applications, nonparametric modeling can provide insight into the features of a dataset that are not obtainable by other means. One successful approach involves the use of (univariate or multivariate) spline spaces. As a class, these methods have inherited much from classical tools for parametric modeling. For example, stepwise variable selection with spline basis terms is a simple scheme for locating knots (breakpoints) in regions where the data exhibit strong, local features. Similarly, candidate knot con gurations (generated by this or some other search technique), are routinely evaluated with traditional selection criteria like AIC or BIC. In short, strategies typically applied in parametric model selection have proved useful in constructing exible, lowdimensional models for nonparametric problems. Until recently, greedy, stepwise procedures were most frequently suggested in the literature. Researchinto Bayesian variable selection, however, has given rise to a number of new splinebased methods that primarily rely on some form of Markov chain Monte Carlo to identify promising knot locations. In this paper, we consider various alternatives to greedy, deterministic schemes, and present aBayesian framework for studying adaptation in the context of an extended linear model (ELM). Our major test cases are Logspline density estimation and (bivariate) Triogram regression models. We selected these because they illustrate a number of computational and methodological issues concerning model adaptation that arise in ELMs.
Zanten, Bayesian inference with rescaled Gaussian process priors, Electron
 Mathematics Institute University of Warwick Coventry
"... Abstract: We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding posteriors ..."
Abstract

Cited by 18 (3 self)
 Add to MetaCart
Abstract: We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding posteriors that contract around the true parameter at optimal convergence rates. To derive our results we establish bounds on small deviation probabilities for smooth stationary Gaussian processes.