| Summary: | 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.�
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