[Springer Texts in Statistics] Essential Statistical Inference Volume 120 || Large Sample Theory: The Basics

✍ Scribed by Boos, Dennis D; Stefanski, L. A

• Book ID: 120032543
• Publisher: Springer New York
• Year: 2012
• Tongue: English
• Weight: 641 KB
• Edition: 2013
• Category: Article
• ISBN: 1461448182
• DOI: 10.1007/978-1-4614-4818-1_5

✦ Synopsis

​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. ​