Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics, 120)

Boos, Dennis D. D.,Stefanski, L A

ISBN 10: 1489987932 / ISBN 13: 9781489987938

Published by Springer, 2015

About this Item

Cover and edges may have some wear.

Seller Inventory # mon0004094009

Bibliographic Details

• Title: Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics, 120)
• Publisher: Springer
• Publication Date: 2015
• Language: English
• ISBN 10: 1489987932
• ISBN 13: 9781489987938
• Binding: paperback
• Condition: Very Good

About this title

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. ​

About the Author

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.