Essential Statistical Inference
Subtitle: Theory and Methods
By: by Dennis D. Boos, L. A. Stefanski
Contributors: Stefanski, L. A.; SpringerLink (Online service)
Series: Springer Texts in Statistics -- 120
Publisher: Springer New York; Imprint: Springer
Published: 2013
Place of publication: New York, NY
Language: eng
Format: [electronic resource] :
Number of pages: 568
ISBN-13: 9781461448181
Subjects: Mathematical statistics; Statistics; Statistics, general; Statistics and Computing/Statistics Programs; Statistical Theory and Methods
Description: ​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.
Table of contents: title: ​ ​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​; title: R-code Index; title: Subject Index. .