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

Boos, Dennis D., Stefanski, Leonard A .

ISBN 10: 1461448174 / ISBN 13: 9781461448174

Edité par Springer, 2013

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N° de réf. du vendeur ERICA77314614481746

Détails bibliographiques

• Titre: Essential Statistical Inference: Theory and Methods (Springer Texts in Statistics)
• Éditeur: Springer
• Date de parution: 2013
• Langue: anglais
• ISBN à 10 chiffres: 1461448174
• ISBN à 13 chiffres: 9781461448174
• Reliure: Hardcover
• État de l'article: Like New
• Type de livre: book

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Synopsis

​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. ​

À propos de l?auteur

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.