Table of contents

  1. Front Matter
    Pages i-xvii
  2. Introductory Material

    1. Front Matter
      Pages 1-1
    2. Denni D Boos, L A Stefanski
      Pages 3-23
  3. Likelihood-Based Methods

    1. Front Matter
      Pages 25-25
    2. Denni D Boos, L A Stefanski
      Pages 27-124
    3. Denni D Boos, L A Stefanski
      Pages 125-161
    4. Denni D Boos, L A Stefanski
      Pages 163-203
  4. Large Sample Approximations in Statistics

    1. Front Matter
      Pages 205-205
    2. Denni D Boos, L A Stefanski
      Pages 207-274
    3. Denni D Boos, L A Stefanski
      Pages 275-293
  5. Methods for Misspecified Likelihoods and Partially Specified Models

    1. Front Matter
      Pages 295-295
    2. Denni D Boos, L A Stefanski
      Pages 297-337
    3. Denni D Boos, L A Stefanski
      Pages 339-359
  6. Computation-Based Methods

    1. Front Matter
      Pages 361-361
    2. Denni D Boos, L A Stefanski
      Pages 363-383
    3. Denni D Boos, L A Stefanski
      Pages 385-411
    4. Denni D Boos, L A Stefanski
      Pages 413-448
    5. Denni D Boos, L A Stefanski
      Pages 449-530
  7. Back Matter
    Pages 531-568

About this book

Introduction

​This book is for students and researchers who have had a first year graduate level mathematical statistics course.  It covers classical likelihood, Bayesian, and permutation inference; an introduction to basic asymptotic distribution theory; and modern topics like M-estimation, the jackknife, and the bootstrap. R code is woven throughout the text, and there are a large number of examples and problems.

An important goal has been to make the topics accessible to a wide audience, with little overt reliance on measure theory.  A typical semester course consists of Chapters 1-6 (likelihood-based estimation and testing, Bayesian inference, basic asymptotic results) plus selections from M-estimation and related testing and resampling methodology.

Dennis Boos and Len Stefanski are professors in the Department of Statistics at North Carolina State. Their research has been eclectic, often with a robustness angle, although Stefanski is also known for research concentrated on measurement error, including a co-authored book on non-linear measurement error models. In recent years the authors have jointly worked on variable selection methods. 

Keywords

Bayesian Inference Data Analysis Jackknife Likelihood Construction Modeling Statistical Inference

Authors and affiliations

  • Dennis D Boos
    • 1
  • L. A Stefanski
    • 2
  1. 1.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  2. 2.Department of StatisticsNorth Carolina State UniversityRaleighUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-4818-1
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-4817-4
  • Online ISBN 978-1-4614-4818-1
  • Series Print ISSN 1431-875X
  • Series Online ISSN 2197-4136
  • About this book