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Essential Statistical Inference
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This book is for students and researchers who have had a first year graduate level mathematicalstatistics 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 numberof examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt relianceon measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimationand 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 forresearch concentrated on measurement error, including a co-authored book on non-linear measurementerror models. In recent years the authors have jointly worked on variable selection methods.
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