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Statistics - Statistical Theory and Methods | Essential Statistical Inference - Theory and Methods

Essential Statistical Inference

Theory and Methods

Series: Springer Texts in Statistics, Vol. 120

Boos, Dennis D., Stefanski, Leonard A .

2013, XVII, 568 p. 34 illus.

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  • Valuable text for graduate students and reference for researchers
  • Contains R code throughout the text and in sample problems
  • Includes unique page references to equation displays

​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. 

Content Level » Graduate

Keywords » Bayesian Inference - Data Analysis - Jackknife - Likelihood Construction - Modeling - Statistical Inference

Related subjects » Computational Statistics - Statistical Theory and Methods - Statistics

Table of contents 

​ ​Roles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions​.- Monte Carlo Simulation Studies​.- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index​.- R-code Index.- Subject Index. 

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