Essential Statistical Inference: Theory and Methods: 120 (Springer Texts in Statistics) - Tapa dura

Críticas

"Boos and Stefanski have written a superb text that fills a void in the Mathematical Statistics genre, an area replete with texts that are either too advanced or too elementary for many statistics graduate students embarking on a research career. To the extent possible, the authors build on advanced calculus and Riemann-Stieltjes integration rather than measure theory and Lebesgue integration to define and support concepts. The authors have mindfully synthesized a wide range of fundamental statistical principles into a single volume and written in a style accessible to first- or second-year statistics graduate students. My colleagues and I have taught from this textbook or earlier iterations for the past six years and students consistently gave the text high marks for its clarity, instructive examples and end-of-chapter exercises. Instructors will also appreciate a chapter dedicated to Monte Carlo simulation studies and presenting numerical results; I have referred students to this chapter on multiple occasions. Essential Statistical Inference is an excellent reference for researchers and an outstanding instructional tool for graduate-level educators." (Brent A. Johnson, Associate Professor, Department of Biostatistics and Bioinformatics, Emory University)

"This modern treatment of graduate-level statistical inference is exceptionally well written. By thoroughly covering modern statistical topics including key computation tools in the same volume as classical material, the authors have produced the ideal textbook for a second-year inference course. The problem-motivated approach makes the book especially attractive to teach from with insightful connections highlighted between topics and across chapters. Through the marriage of historical descriptions of central questions in classical statistics with Maple and R code for examples and experiments, this text is certain to become a widely used reference book." (Taki Shinohara, Assistant Professor of Biostatistics, University of Pennsylvania)

Reseña del editor

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.