Essential Statistical Inference : Theory and Methods PDF
by Dennis D. Boos, L A Stefanski
Part of the Springer Texts in Statistics series
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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. ?
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- Format:PDF
- Publisher:Springer New York
- Publication Date:06/02/2013
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- ISBN:9781461448181
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Download Now
- Format:PDF
- Publisher:Springer New York
- Publication Date:06/02/2013
- Category:
- ISBN:9781461448181