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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I only selectively read about 80% of all the materials. Very practical and comprehensive, a must-read to learn about more advanced statistical inference tools.
评分写的非常非常好，读完之后，可以对统计推断有个较为全面的认识。需要一定的基础才能读·~·
评分写的非常非常好，读完之后，可以对统计推断有个较为全面的认识。需要一定的基础才能读·~·
评分I only selectively read about 80% of all the materials. Very practical and comprehensive, a must-read to learn about more advanced statistical inference tools.
评分5.2 Essential Statistical Inference - Boos and Stefanski (Springer, 2013)
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