DOCSLIB.ORG

Nonparametric Bayes: Inference Under Nonignorable Missingness and Model Selection

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Nonparametric Bayes: Inference Under Nonignorable Missingness and Model Selection NONPARAMETRIC BAYES: INFERENCE UNDER NONIGNORABLE MISSINGNESS AND MODEL SELECTION By ANTONIO LINERO A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2015 c 2015 Antonio Linero Dedicated to Mike and Hani for their gracious support, and Katie for her patience! ACKNOWLEDGMENTS I would first like to convey my sincerest gratitude to my advisers, Professor Michael J. Daniels and Professor Hani Doss, for their help and encouragement. I have been fortunate to have two excellent mentors to bounce ideas off of, and to offer assurances when I doubted my work. They have provided me with every opportunity to succeed, and it has been an honor to work with them. I would also like to thank my committee members, Professor Malay Ghosh who always had an open door and Professor Arunava Banerjee for providing a valuable Machine Learning perspective. Outside of my committee, I am grateful to Professor Daniel O. Scharfstein for his insights into the missing data problem and for giving me the opportunity to visit Johns Hopkins. I am also particularly grateful to Professor Ron Randles, whose Introduction to Mathematical Statistics course provided the initial inspiration for me to pursue Statistics, and to Professor Andrew Rosalsky for providing me with a deep appreciation of Probability. Lastly, I would like to thank my partner Katie for her inspiration and patience, and my parents, without whose support I would not have had the opportunity to succeed. 4 TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................4 LIST OF TABLES.....................................8 LIST OF FIGURES....................................9 ABSTRACT........................................ 10 CHAPTER 1 PRELIMINARIES ON BAYESIAN NONPARAMETRICS............ 12 1.1 Introduction................................... 12 1.2 Posterior Consistency.............................. 13 1.3 Review of Random Measures.......................... 15 1.3.1 Dirichlet Processes........................... 15 1.3.2 Mixtures of Dirichlet Processes and Dirichlet Process Mixtures... 17 1.3.3 Dependent Random Measures..................... 19 2 INFORMATIVE MISSINGNESS IN LONGITUDINAL STUDIES........ 20 2.1 Introduction................................... 20 2.2 Notation..................................... 21 2.3 Rubin's Classification of Missing Data.................... 23 2.4 Why Bayesian Nonparametrics?........................ 25 2.5 Existing Approaches.............................. 26 2.5.1 Likelihood Factorizations........................ 26 2.5.2 Non-Likelihood Based Approaches................... 29 2.6 Identifying Restrictions and Sensitivity Parameters............. 30 2.7 Partial and Latent Ignorability........................ 33 2.8 Intermittent Missingness............................ 34 2.9 Summary of Our Strategy and Our Contributions.............. 35 3 NONPARAMETRIC BAYES FOR NONIGNORABLE MISSINGNESS..... 37 3.1 Introduction................................... 37 3.2 Strategy for Prior Specification........................ 39 3.3 Posterior Consistency of pobs .......................... 40 3.4 Kullback-Leibler Property for Kernel Mixture Models............ 44 3.5 Identifying Restrictions............................. 49 3.5.1 Monotone Missingness......................... 49 3.5.2 Non-monotone Missingness....................... 51 3.6 Inference by G-Computation.......................... 53 3.7 Discussion.................................... 56 5 4 A DIRICHLET PROCESS MIXTURE WORKING MODEL, WITH APPLICATION TO A SCHIZOPHRENIA CLINICAL TRIAL................... 57 4.1 Introduction................................... 57 4.2 The Schizophrenia Clinical Trial........................ 57 4.3 A Dirichlet Process Mixture Working Prior.................. 59 4.4 The Extrapolation Distribution........................ 61 4.5 Computation and Inference.......................... 62 4.6 Simulation Studies............................... 63 4.6.1 Performance for Mean Estimation under MAR............ 64 4.6.2 Performance for Effect Estimation Under MNAR........... 66 4.7 Application to the Schizophrenia Clinical Trial................ 69 4.7.1 Comparison to Alternatives and Assessing Model Fit........ 69 4.7.2 Inference and Sensitivity Analysis................... 71 4.8 Discussion.................................... 75 5 EMPIRICAL BAYES ESTIMATION AND MODEL SELECTION FOR HIERARCHICAL NONPARAMETRIC PRIORS............................ 77 5.1 Introduction................................... 77 5.1.1 Motivating Examples.......................... 81 5.1.2 Our Contributions............................ 82 5.2 Theoretical Development............................ 83 5.2.1 Marginal Likelihoods.......................... 84 5.2.2 Limiting Cases of the Hierarchical Dirichlet Process......... 87 5.3 Estimation of Bayes Factor Surfaces...................... 91 5.3.1 Testing Against Boundary Values................... 95 5.3.2 Empirical Bayes Estimation...................... 98 5.4 Illustrations................................... 100 5.4.1 Quality of Hospital Care Data..................... 100 5.4.2 Topic Modeling............................. 104 5.5 Discussion.................................... 109 6 DISCUSSION AND FUTURE WORK....................... 110 6.1 Rates of Convergence.............................. 110 6.2 More Work on Non-monotone Missingness.................. 110 6.3 Multivariate Models for Missing Data..................... 111 6.4 Causal Inference................................ 111 6.5 Alternatives to the Hierarchical Dirichlet Process.............. 112 APPENDIX A APPENDIX TO CHAPTER 3............................ 113 A.1 Proof of Theorem 3.2.............................. 113 A.2 Proof of Theorem 3.3.............................. 113 6 A.3 Proof of Theorem 3.5.............................. 119 B APPENDIX TO CHAPTER 4............................ 122 B.1 Blocked Gibbs Sampler............................. 122 B.2 Prior Specification............................... 123 B.2.1 Parametric Priors............................ 123 B.2.2 Nonparametric Default Priors..................... 123 B.3 Simulation Settings............................... 124 B.3.1 Section 4.6.1............................... 124 B.3.2 Section 4.6.2............................... 125 B.4 Exponential Tilting............................... 130 C APPENDIX TO CHAPTER 5............................ 133 C.1 Proof of Theorem 5.2.............................. 133 C.2 Proof of Lemma 5.1............................... 133 C.3 Proof of Lemma 5.2............................... 134 C.4 Impropriety of Posterior Under an Improper Prior.............. 135 REFERENCES....................................... 137 BIOGRAPHICAL SKETCH................................ 146 7 LIST OF TABLES Table page 2-1 Schematic representation of ACMV.......................... 31 2-2 Schematic representation of NFD........................... 32 4-1 Simulation results under MAR............................ 66 4-2 Comparison of results on SCT data under MAR.................. 70 B-1 Results from simulation study in Section 4.6.2.................... 128 8 LIST OF FIGURES Figure page 3-1 Schematic describing the working prior framework................. 40 3-2 Graphical depiction of the coupling interpretation of the transformation method. 50 4-1 Trajectories of two latent classes........................... 59 4-2 Results from simulation study in Section 4.6.2.................... 68 4-3 Model checking for SCT................................ 71 4-4 Improvement of treatments relative to placebo................... 74 4-5 Contour plot for treatment effects as functions of sensitivity parameters..... 75 5-1 Graphical depiction of a Bayesian hierarchical data generating mechanism.... 78 5-2 Graphical representation of the HDP as a directed acyclic graph......... 79 5-3 Draws from a Markov chain targeting improper posterior of γ........... 80 5-4 Models corresponding to boundary values of (α; γ)................. 89 5-5 Models obtained by letting α or γ tend to 0 or ................. 90 1 5-6 MCMC output for (α; γ) under an informative prior................ 102 5-7 Logarithm of Bayes factor surface of (α; γ)...................... 103 5-8 True topics used in simulation experiment...................... 105 5-9 Histogram of samples from the posterior distribution of α and γ.......... 106 5-10 L1 error in estimating the most prevalent topic................... 107 5-11 Sensitivity of estimation of the most prevalent topic to choice of hyperparameter. 108 B-1 Dataset generated under M2............................. 129 B-2 Dataset generated under M3............................. 130 9 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy NONPARAMETRIC BAYES: INFERENCE UNDER NONIGNORABLE MISSINGNESS AND MODEL SELECTION By Antonio Linero August 2015 Chair: Michael J. Daniels Cochair: Hani Doss Major: Statistics This dissertation concerns two essentially independent topics, with the primary link between the two being the use of Bayesian nonparametrics as an inference tool. The first topic concerns inference in the presence of missing data, with emphasis on longitudinal clinical trials with attrition. In this setting, it is well known that many effects of interest are not identified in the absence of untestable assumptions; the best one can do is to conduct a sensitivity analysis to determine
Load more

Recommended publications
  • Superprocesses and Mckean-Vlasov Equations with Creation of Mass
    Sup erpro cesses and McKean-Vlasov equations with creation of mass L. Overb eck Department of Statistics, University of California, Berkeley, 367, Evans Hall Berkeley, CA 94720, y U.S.A. Abstract Weak solutions of McKean-Vlasov equations with creation of mass are given in terms of sup erpro cesses. The solutions can b e approxi- mated by a sequence of non-interacting sup erpro cesses or by the mean- eld of multityp e sup erpro cesses with mean- eld interaction. The lat- ter approximation is asso ciated with a propagation of chaos statement for weakly interacting multityp e sup erpro cesses. Running title: Sup erpro cesses and McKean-Vlasov equations . 1 Intro duction Sup erpro cesses are useful in solving nonlinear partial di erential equation of 1+ the typ e f = f , 2 0; 1], cf. [Dy]. Wenowchange the p oint of view and showhowtheyprovide sto chastic solutions of nonlinear partial di erential Supp orted byanFellowship of the Deutsche Forschungsgemeinschaft. y On leave from the Universitat Bonn, Institut fur Angewandte Mathematik, Wegelerstr. 6, 53115 Bonn, Germany. 1 equation of McKean-Vlasovtyp e, i.e. wewant to nd weak solutions of d d 2 X X @ @ @ + d x; + bx; : 1.1 = a x; t i t t t t t ij t @t @x @x @x i j i i=1 i;j =1 d Aweak solution = 2 C [0;T];MIR satis es s Z 2 t X X @ @ a f = f + f + d f + b f ds: s ij s t 0 i s s @x @x @x 0 i j i Equation 1.1 generalizes McKean-Vlasov equations of twodi erenttyp es.
    [Show full text]
  • Birth and Death Process in Mean Field Type Interaction
    BIRTH AND DEATH PROCESS IN MEAN FIELD TYPE INTERACTION MARIE-NOÉMIE THAI ABSTRACT. Theaim ofthispaperis to study theasymptoticbehaviorofa system of birth and death processes in mean field type interaction in discrete space. We first establish the exponential convergence of the particle system to equilibrium for a suitable Wasserstein coupling distance. The approach provides an explicit quantitative estimate on the rate of convergence. We prove next a uniform propagation of chaos property. As a consequence, we show that the limit of the associated empirical distribution, which is the solution of a nonlinear differential equation, converges exponentially fast to equilibrium. This paper can be seen as a discrete version of the particle approximation of the McKean-Vlasov equations and is inspired from previous works of Malrieu and al and Caputo, Dai Pra and Posta. AMS 2000 Mathematical Subject Classification: 60K35, 60K25, 60J27, 60B10, 37A25. Keywords: Interacting particle system - mean field - coupling - Wasserstein distance - propagation of chaos. CONTENTS 1. Introduction 1 Long time behavior of the particle system 4 Propagation of chaos 5 Longtimebehaviorofthenonlinearprocess 7 2. Proof of Theorem1.1 7 3. Proof of Theorem1.2 12 4. Proof of Theorem1.5 15 5. Appendix 16 References 18 1. INTRODUCTION The concept of mean field interaction arised in statistical physics with Kac [17] and then arXiv:1510.03238v1 [math.PR] 12 Oct 2015 McKean [21] in order to describe the collisions between particles in a gas, and has later been applied in other areas such as biology or communication networks. A particle system is in mean field interaction when the system acts over one fixed particle through the em- pirical measure of the system.
    [Show full text]
  • 1 Introduction
    The analysis of marked and weighted empirical processes of estimated residuals Vanessa Berenguer-Rico Department of Economics; University of Oxford; Oxford; OX1 3UQ; UK and Mansfield College; Oxford; OX1 3TF [email protected] Søren Johansen Department of Economics; University of Copenhagen and CREATES; Department of Economics and Business; Aarhus University [email protected] Bent Nielsen1 Department of Economics; University of Oxford; Oxford; OX1 3UQ; UK and Nuffield College; Oxford; OX1 1NF; U.K. bent.nielsen@nuffield.ox.ac.uk 29 April 2019 Summary An extended and improved theory is presented for marked and weighted empirical processes of residuals of time series regressions. The theory is motivated by 1- step Huber-skip estimators, where a set of good observations are selected using an initial estimator and an updated estimator is found by applying least squares to the selected observations. In this case, the weights and marks represent powers of the regressors and the regression errors, respectively. The inclusion of marks is a non-trivial extention to previous theory and requires refined martingale arguments. Keywords 1-step Huber-skip; Non-stationarity; Robust Statistics; Stationarity. 1 Introduction We consider marked and weighted empirical processes of residuals from a linear time series regression. Such processes are sums of products of an adapted weight, a mark that is a power of the innovations and an indicator for the residuals belonging to a half line. They have previously been studied by Johansen & Nielsen (2016a) - JN16 henceforth - generalising results by Koul & Ossiander (1994) and Koul (2002) for processes without marks. The results presented extend and improve upon expansions previously given in JN16, while correcting a mistake in the argument, simplifying proofs and allowing weaker conditions on the innovation distribution and regressors.
    [Show full text]
  • Analysis of Error Propagation in Particle Filters with Approximation
    The Annals of Applied Probability 2011, Vol. 21, No. 6, 2343–2378 DOI: 10.1214/11-AAP760 c Institute of Mathematical Statistics, 2011 ANALYSIS OF ERROR PROPAGATION IN PARTICLE FILTERS WITH APPROXIMATION1 By Boris N. Oreshkin and Mark J. Coates McGill University This paper examines the impact of approximation steps that be- come necessary when particle filters are implemented on resource- constrained platforms. We consider particle filters that perform in- termittent approximation, either by subsampling the particles or by generating a parametric approximation. For such algorithms, we de- rive time-uniform bounds on the weak-sense Lp error and present associated exponential inequalities. We motivate the theoretical anal- ysis by considering the leader node particle filter and present numeri- cal experiments exploring its performance and the relationship to the error bounds. 1. Introduction. Particle filters have proven to be an effective approach for addressing difficult tracking problems [8]. Since they are more compu- tationally demanding and require more memory than most other filtering algorithms, they are really only a valid choice for challenging problems, for which other well-established techniques perform poorly. Such problems in- volve dynamics and/or observation models that are substantially nonlinear and non-Gaussian. A particle filter maintains a set of “particles” that are candidate state values of the system (e.g., the position and velocity of an object). The filter evaluates how well individual particles correspond to the dynamic model and set of observations, and updates weights accordingly. The set of weighted particles provides a pointwise approximation to the filtering distribution, which represents the posterior probability of the state.
    [Show full text]
  • The Tail Empirical Process of Regularly Varying Functions of Geometrically Ergodic Markov Chains Rafal Kulik, Philippe Soulier, Olivier Wintenberger
    The tail empirical process of regularly varying functions of geometrically ergodic Markov chains Rafal Kulik, Philippe Soulier, Olivier Wintenberger To cite this version: Rafal Kulik, Philippe Soulier, Olivier Wintenberger. The tail empirical process of regularly vary- ing functions of geometrically ergodic Markov chains. Stochastic Processes and their Applications, Elsevier, 2019, 129 (11), pp.4209-4238. 10.1016/j.spa.2018.11.014. hal-01228825v2 HAL Id: hal-01228825 https://hal.archives-ouvertes.fr/hal-01228825v2 Submitted on 21 Sep 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The tail empirical process of regularly varying functions of geometrically ergodic Markov chains Rafa lKulik∗ Philippe Soulier† Olivier Wintenberger‡ September 21, 2018 Abstract We consider a stationary regularly varying time series which can be expressed as a function of a geometrically ergodic Markov chain. We obtain practical con- ditions for the weak convergence of the tail array sums and feasible estimators of cluster statistics. These conditions include the so-called geometric drift or Foster- Lyapunov condition and can be easily checked for most usual time series models with a Markovian structure. We illustrate these conditions on several models and statis- tical applications.
    [Show full text]
  • Specification Tests for the Error Distribution in GARCH Models
    Computational Statistics and Data Analysis 56 (2012) 3587–3598 Contents lists available at SciVerse ScienceDirect Computational Statistics and Data Analysis journal homepage: www.elsevier.com/locate/csda Specification tests for the error distribution in GARCH models B. Klar a,∗, F. Lindner a, S.G. Meintanis b a Institut für Stochastik, Karlsruhe Institute of Technology (KIT), Kaiserstraße 89, 76133 Karlsruhe, Germany b Department of Economics, National and Kapodistrian University of Athens, 8 Pesmazoglou Street, 105 59 Athens, Greece article info a b s t r a c t Article history: Goodness-of-fit and symmetry tests are proposed for the innovation distribution in Received 19 November 2009 generalized autoregressive conditionally heteroscedastic models. The tests utilize an Received in revised form 18 May 2010 integrated distance involving the empirical characteristic function (or the empirical Laplace Accepted 19 May 2010 transform) computed from properly standardized observations. A bootstrap version of Available online 12 June 2010 the tests serves the purpose of studying the small sample behaviour of the proclaimed procedures in comparison with more classical approaches. Finally, all tests are applied to Keywords: some financial data sets. GARCH model Goodness-of-fit test ' 2010 Elsevier B.V. All rights reserved. Symmetry test Empirical characteristic function Bootstrap test 1. Introduction Suppose that a process is driven by a generalized autoregressive conditionally heteroscedastic (GARCH) model of specific order. This class of models was introduced by Bollerslev(1986), and despite the fact that certain properties of GARCH processes make no use of the particular form of the innovation distribution (see, for example, the stationarity conditions mentioned later in this section), GARCH models with Gaussian innovations have been considered.
    [Show full text]
  • Notes on Counting Process in Survival Analysis
    Notes on Counting Process in Survival Analysis Mai Zhou Distribution of some statistics, especially rank based, with censored data are hard to get. Even if they can be worked out for small samples, they are likely to be prohibitively expensive for larger sample sizes. Therefore we need to work on the asymptotic distribution - which often works well for moderate and large sample sizes. This notes is intended to be read along with Fleming and Harrington's book Counting Pro- cesses and Survival Analysis. It is not intended as a rigorous treatment of the subject of counting process martingale. The aim is to (1) present intuitions to help visualize the counting process and (2) supply simplified proofs (in special cases, or with more assumptions, perhaps), make the book easier to read and (3) select the materials that can be realistically covered in one semester or even one quarter. The students need only to have had background in Poisson process and basic probability/statistics. Some prior knowledge of survival analysis will help understand the example/application of counting process in the survival analysis. There is an equally good book on the counting processes: by Andersen, Borgan, Keiding, Gill Statistical Models Based on Counting Processes, Springer 1993 This book contains many more examples of application. The book also has more materials then can be covered in one semester. At a math level similar to FH book. Chapter 5 of Kalbflisch and Prentice, second edition is similar to this note. Review of Master level Survival Analysis. Review of central limit theorem (Linderberg condi- tion) and Poisson process (outlined below).
    [Show full text]
  • Heavy Tailed Analysis Eurandom Summer 2005
    HEAVY TAILED ANALYSIS EURANDOM SUMMER 2005 SIDNEY RESNICK School of Operations Research and Industrial Engineering Cornell University Ithaca, NY 14853 USA [email protected] http://www.orie.cornell.edu/ sid ∼ & Eurandom http://www.eurandom.tue.nl/people/EURANDOM_chair/eurandom_chair.htm 1 60 SIDNEY RESNICK 8. Asymptotic Normality of the Tail Empirical Measure As in Section 7.1, suppose X , j 1 are iid, non-negative random variables with common { j ≥ } distribution F (x), where F¯ RV−α for α > 0. Continue with the notation in (7.1), (7.2), and (7.3). Define the tail empirical∈ process, 1 n n (8.1) W (y)=√k ǫ (y−1/α, ] F¯(b(n/k)y−1/α) n k Xi/b(n/k) ∞ − k i=1 X =√k ν (y−1/α, ] E(ν (y−1/α, ]) . n ∞ − n ∞ Theorem 7. If (7.1), (7.2), and (7.3) hold, then in D[0, ) ∞ W W n ⇒ where W is Brownian motion on [0, ). ∞ Remark 3. Note, because of regular variation, as n , k/n 0, → ∞ → n −α (8.2) Eν (y−1/α, ]= F¯(b(n/k)y−1/α) y−1/α = y. n ∞ k → For applications to such things as the asymptotic normality of the Hill estimator and other estimators derived from the tail empirical measure, we would prefer the centering in (8.1) be y. However, to make this substitution in (8.1) requires knowing or assuming n (8.3) lim √k F¯(b(n/k)y−1/α) y n→∞ k − exists finite. This is one of the origins for the need of second order regular variation.
    [Show full text]
  • Empirical Processes with Applications in Statistics 3
    Empirical Processes with Applications in Statistics 3. Glivenko–Cantelli and Donsker Theorems Johan Segers UCLouvain (Belgium) Conférence Universitaire de Suisse Occidentale Programme Doctoral en Statistique et Probabilités Appliquées Les Diablerets, February 4–5, 2020 1 / 54 Set-up I i.i.d. random variables X1; X2;::: on some probability space Ω taking values in some measurable space (X; A) with common law P P(Xi 2 B) = P(B); 8B 2 A I Family F ⊂ L1(P) of P-integrable functions f : X! R I Empirical and population measures: for f 2 F , 1 Xn P f = f(X ) n n i i=1 Z Pf = f(x) dP(x) = E[f(Xi)] X I Empirical process: p Gn = n (Pn − P) 2 / 54 X e t z a à à I M 3 / 54 Bounded stochastic processes Assume that the following map is bounded almost surely: F! R : f 7! Pnf − Pf Then we can view Pn − P and Gn as (possibly non-measurable) maps Ω ! `1(F ) I Assume that supf2F jf(x)j < 1 for P-almost every x 2 X and supf2F jPfj < 1 I Often, we will assume that F has a P-integrable envelope F : X! [0; 1), i.e., 8x 2 X; 8f 2 F ; jf(x)j 6 F(x) 4 / 54 a q s f a o X z 5 / 54 Uniform law of large numbers: Glivenko–Cantelli Strong law of large numbers. For all f 2 F , Pnf ! Pf a.s.; n ! 1 Trivially, for every f1;:::; fk 2 F , max jPnf − Pfj ! 0 a.s.; n ! 1 j=1;:::;k Can we make this statement uniform in F ? Definition: Glivenko–Cantelli.
    [Show full text]
  • Introduction to Empirical Processes and Semiparametric Inference1
    This is page i Printer: Opaque this Introduction to Empirical Processes and Semiparametric Inference1 Michael R. Kosorok August 2006 1c 2006 SPRINGER SCIENCE+BUSINESS MEDIA, INC. All rights re- served. Permission is granted to print a copy of this preliminary version for non- commercial purposes but not to distribute it in printed or electronic form. The author would appreciate notification of typos and other suggestions for improve- ment. ii This is page iii Printer: Opaque this Preface The goal of this book is to introduce statisticians, and other researchers with a background in mathematical statistics, to empirical processes and semiparametric inference. These powerful research techniques are surpris- ingly useful for studying large sample properties of statistical estimates from realistically complex models as well as for developing new and im- proved approaches to statistical inference. This book is more a textbook than a research monograph, although some new results are presented in later chapters. The level of the book is more introductory than the seminal work of van der Vaart and Wellner (1996). In fact, another purpose of this work is to help readers prepare for the mathematically advanced van der Vaart and Wellner text, as well as for the semiparametric inference work of Bickel, Klaassen, Ritov and Wellner (1997). These two books, along with Pollard (1990) and chapters 19 and 25 of van der Vaart (1998), formulate a very complete and successful elucida- tion of modern empirical process methods. The present book owes much by the way of inspiration, concept, and notation to these previous works. What is perhaps new is the introductory, gradual and unified way this book introduces the reader to the field.
    [Show full text]
  • Bayesian Nonparametric Models
    Bayesian Nonparametric Models Peter Orbanz, Cambridge University Yee Whye Teh, University College London Related keywords: Bayesian Methods, Prior Probabilities, Dirichlet Process, Gaussian Processes. Definition A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter space. The parameter space is typically chosen as the set of all possi- ble solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions, and in a density esti- mation problem the space can consist of all densities. A Bayesian nonparametric model uses only a finite subset of the available parameter dimensions to explain a finite sample of observations, with the set of dimensions chosen depending on the sample, such that the effective complexity of the model (as measured by the number of dimensions used) adapts to the data. Classical adaptive problems, such as nonparametric estimation and model selection, can thus be formulated as Bayesian inference problems. Popular examples of Bayesian nonparametric models include Gaussian process regression, in which the correlation structure is refined with growing sample size, and Dirichlet process mixture models for clustering, which adapt the number of clusters to the complexity of the data. Bayesian nonparametric models have recently been applied to a variety of ma- chine learning problems, including regression, classification, clustering, latent variable modeling, sequential modeling, image segmentation, source separation and grammar induction. Motivation and Background Most of machine learning is concerned with learning an appropriate set of pa- rameters within a model class from training data. The meta level problems of determining appropriate model classes are referred to as model selection or model adaptation.
    [Show full text]
  • Hierarchical Dirichlet Processes
    Hierarchical Dirichlet Processes Ye e W hye T EH, Michael I. JORDAN, Matthew J. BEAL, and David M. BLEI We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the well-known clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models in the different groups necessarily share mixture components. We discuss representations of hierarchical Dirichlet processes in terms of a stick-breaking process, and a generalization of the Chinese restaurant process that we refer to as the “Chinese restaurant franchise.” We present Markov chain Monte Carlo algorithms for posterior inference in hierarchical Dirichlet process mixtures and describe applications to problems in information retrieval and text modeling. KEY WORDS: Clustering; Hierarchical model; Markov chain Monte Carlo; Mixture model; Nonparametric Bayesian statistics. 1. INTRODUCTION would have significant implications for the understanding of the migration patterns of ancestral populations of humans.
    [Show full text]