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

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

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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.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 1489987932
Category: Books > Subjects > Computing & Internet > Software & Graphics > Business & Home Office
Domain: Amazon UK
Release Date: 06 March 2015
Listed Since: 07 March 2015

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

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