Mathematical Statistics: Basic Ideas and Selected Topics, Volume I, Second Edition (Chapman & Hall/CRC Texts in Statistical Science) (Volume 2)
By 作者: Peter J. Bickel - Kjell A. Doksum
ISBN-10 书号: 1498723802
ISBN-13 书号: 9781498723800
Edition 版本: 2
Release Finelybook 出版日期: 2015-04-13
Pages 页数: 576
The Book Description robot was collected from Amazon and arranged by Finelybook
Mathematical Statistics: Basic Ideas and Selected Topics, Volume II presents important statistical concepts, methods, and tools not covered in the authors’ previous volume. This second volume focuses on inference in non- and semiparametric models. It not only reexamines the procedures introduced in the first volume from a more sophisticated point of view but also addresses new problems originating from the analysis of estimation of functions and other complex decision procedures and large-scale data analysis.
The book covers asymptotic efficiency in semiparametric models from the Le Cam and Fisherian points of view as well as some finite sample size optimality criteria based on Lehmann–Scheffé theory. It develops the theory of semiparametric maximum likelihood estimation with applications to areas such as survival analysis. It also discusses methods of inference based on sieve models and asymptotic testing theory. The remainder of the book is devoted to model and variable selection, Monte Carlo methods, nonparametric curve estimation, and prediction, classification, and machine learning topics. The necessary background material is included in an appendix.
Using the tools and methods developed in this textbook, students will be ready for advanced research in modern statistics. Numerous examples illustrate statistical modeling and inference concepts while end-of-chapter problems reinforce elementary concepts and introduce important new topics. As in Volume I, measure theory is not required for understanding.
The solutions to exercises for Volume II are included in the back of the book.
Check out Volume I for fundamental, classical statistical concepts leading to the material in this volume.
PREFACE TO THE 2015 EDITION
Chapter I-INTRODUCTION AND EXAMPLES
Chapter 7-TOOLS FOR ASYMPTOTIC ANALYSIS
Chapter 8-DISTRIBUTION-FREE,UNBIASED,AND EQUIVARIANT PROCEDURES
Chapter 9-INFERENCE IN SEMIPARAMETRIC MODELS
Chapter 10-MONTE CARLO METHODS
Chapter 11-NONPARAMETRIC INFERENCE FOR FUNCTIONS OF ONE VARIABLE
Chapter 12-PREDICTION AND MACHINE LEARNING
Appendix D-SOME AUXILIARY RESULTS
Appendix E-SOLUTIONS FOR VOLUME lI
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