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

Boos, Dennis D., Stefanski, Leonard A .

ISBN 10: 1461448174 / ISBN 13: 9781461448174

Published by Springer, 2013

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Bibliographic Details

• Title: Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics)
• Publisher: Springer
• Publication Date: 2013
• Language: English
• ISBN 10: 1461448174
• ISBN 13: 9781461448174
• Binding: Hardcover
• Condition: Like New
• Book Type: book

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.