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Essential Statistical Inference

Essential Statistical Inference


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About the Book

​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. ​


About the Author: 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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Product Details
  • ISBN-13: 9781489987938
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Height: 234 mm
  • No of Pages: 568
  • Series Title: Springer Texts in Statistics
  • Sub Title: Theory and Methods
  • Width: 156 mm
  • ISBN-10: 1489987932
  • Publisher Date: 06 Mar 2015
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Spine Width: 30 mm
  • Weight: 861 gr


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