Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
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From the reviews: "Introduction to Empirical Processes and Semiparametric Inference is a very good combination of both the empirical processes and semiparametric theories. This is the first book of its kind....I agree with the author that this book is 'more of a textbook than a research monograph.' As the semiparametric inference is currently an extremely active research area in statistical research, the book will open the door for graduate students to identify significant future research potentials. In fact, this book contains the author's newest research result, the application of semiparametric method in microarray data analysis. This book can be used as a textbook for graduate students in statistics, biostatistics, and economics (econometrics). In fact, the contents of this book can be tailored for different courses." "Generally, this is a great book on empirical processes and semiparametric methods. It should be on the must-read list for a serious statistician, biostatistician, or econometrician." (Biometrics, September 2008) "The main focus of this book is to introduce empirical processes and semiparametric inference methods to researchers interested in developing inferential tools for relatively complicated mathematical or statistical modeling problems. ...The material is structured in a sensible way supporting the learning and understanding of useful and challenging techniques of empirical processes and semiparametric inference. The book could well be very helpful for those studying and applying these techniques." (International Statistical Review 2008,77,2) “This book is an introduction to what is commonly called the modern theory of empirical processes – empirical processes indexed by classes of functions – and to semiparametric inference, and the interplay between both fields. … This is clearly intended to be a book for the novice in empirical process theory and semiparametric inference. … The main material is presented in a clearly arranged and logical order. … will be useful to anybody who wants to learn about the modern theory of empirical processes and semiparametric inference.” (Erich Häusler, Zentralblatt MATH, Vol. 1180, 2010)
From the Back Cover
This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level of the book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability. The book consists of three parts. The first part is a concise overview of all of the main concepts covered in the book with a minimum of technicalities. The second and third parts cover the two respective main topics of empirical processes and semiparametric inference in depth. The connections between these two topics is also demonstrated and emphasized throughout the text. Each part has a final chapter with several case studies that use concrete examples to illustrate the concepts developed so far. The last two parts also each include a chapter which covers the needed mathematical preliminaries. Each main idea is introduced with a non-technical motivation, and examples are given throughout to illustrate important concepts. Homework problems are also included at the end of each chapter to help the reader gain additional insights. Michael R. Kosorok is Professor and Chair, Department of Biostatistics, and Professor, Department of Statistics and Operations Research, at the University of North Carolina at Chapel Hill. His research has focused on the application of empirical processes and semiparametric inference to statistics and biostatistics. He is a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. He is an Associate Editor of the Annals of Statistics, Electronic Journal of Statistics, International Journal of Biostatistics, Statistics and Probability Letters, and Statistics Surveys.
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Key Phrases - Statistically Improbable Phrases (SIPs):
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entropy calculations, argmax theorem, piggyback bootstrap, finitely discrete probability measures, efficient influence function, inner probability one, misspecified link function, continuous mapping results, multiplier central limit theorem, bracketing entropy, profile sampler, asymptotic measurability, empirical process results, asymptotic tightness, empirical process methods, multinomial bootstrap, uniform entropy integral, empirical process techniques, counting process regression, functional delta method, weighted bootstrap, bootstrap validity, pointwise measurable, integrable envelope, bootstrap weights Key Phrases - Capitalized Phrases (CAPs): (learn more)
Case Studies, Proof of Lemma, Bootstrapping Empirical Processes, Additional Empirical Process Results, Semiparametric M-Estimation, Proof of Corollary, Monte Carlo, The Functional Delta Method, Non-Regular Examples, Notes Theorem, Overview of Semiparametric Inference, Cramér-von Mises, Examples Continued, Temporal Process Regression, Other Modes of Convergence, Identically Distributed Stochastic Processes, Efficient Linear Regression, Semiparametric Maximum Likelihood Estimation, Assume Conditions, Master Theorem, Logistic Regression Example Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
entropy calculations, argmax theorem, piggyback bootstrap, finitely discrete probability measures, efficient influence function, inner probability one, misspecified link function, continuous mapping results, multiplier central limit theorem, bracketing entropy, profile sampler, asymptotic measurability, empirical process results, asymptotic tightness, empirical process methods, multinomial bootstrap, uniform entropy integral, empirical process techniques, counting process regression, functional delta method, weighted bootstrap, bootstrap validity, pointwise measurable, integrable envelope, bootstrap weights Key Phrases - Capitalized Phrases (CAPs): (learn more)
Case Studies, Proof of Lemma, Bootstrapping Empirical Processes, Additional Empirical Process Results, Semiparametric M-Estimation, Proof of Corollary, Monte Carlo, The Functional Delta Method, Non-Regular Examples, Notes Theorem, Overview of Semiparametric Inference, Cramér-von Mises, Examples Continued, Temporal Process Regression, Other Modes of Convergence, Identically Distributed Stochastic Processes, Efficient Linear Regression, Semiparametric Maximum Likelihood Estimation, Assume Conditions, Master Theorem, Logistic Regression Example Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Surprise Me!
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