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Field name	Details
Dewey Class	519.5
Title	Introduction to Empirical Processes and Semiparametric Inference (EB) / by Michael R. Kosorok.
Author	Kosorok, Michael R.
Other name(s)	SpringerLink (Online service)
Publication	New York, NY : Springer , 2008.
Physical Details	: online resource.
Series	Springer Series in Statistics 0172-7397
ISBN	9780387749785
Summary Note	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.:
Contents note	Introduction -- An Overview of The Empirical Processes -- Overview of Semiparametric Inference -- Case Studies I -- Introduction to Empirical Processes -- Preliminiaries for Empirical Processes -- Stochastic Convergence -- Empirical Process Methods -- Entropy Calculations -- Bootstrapping Empirical Processes -- Additional Empirical Process Results -- The Functional Delta Method -- Z-Estimators -- M-Estimators -- Case Studies II -- Introduction To Semiparametric Inference -- Seimparametric Models and Efficiency -- Efficient Inference for Fininte-Dimensional Parameters -- Efficient Inference for Infinite-Dimensional Parameters -- Semiparametric M-Estimators -- Case Studies III.
System details note	Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
Internet Site	http://dx.doi.org/10.1007/978-0-387-74978-5
Links to Related Works	Subject References: Mathematical Statistics . Statistical Theory and Methods . Statistics . Authors: Kosorok, Michael R. . Corporate Authors: SpringerLink (Online service) . Series: Springer Series in Statistics . Classification: 519.5 .

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Introduction to Empirical Processes and Semiparametric Inference

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