Essential Statistical Inference: Theory and Methods

By L.A. Stefanski and Dennis D. Boos

Book Overview

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.

• Format: Hardcover
• Language: English
• ISBN: 1461448174
• ISBN13: 9781461448174
• Release Date: February 2013
• Publisher: Springer
• Length: 568 Pages
• Weight: 1.00 lbs.
• Dimensions: 1.3" x 6.1" x 9.2"