Can I read Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics (120)) on EtoBox?

Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics (120)) by Dennis D Boos; Leonard A Stefanski is a nonfiction available to read on EtoBox.

What is Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics (120)) about?

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.

Who reads Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics (120))?

Author

Dennis D Boos; Leonard A Stefanski

Publisher

Springer US

Published

2013

Language

EN

ISBN

9781461448181

Category

nonfiction

Subjects

Mathematics, Mathematical Statistics, Stem