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Essential Statistical Inference: Theory and Methods /

Essential Statistical Inference: Theory and Methods /
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Field name Details
Dewey Class 519.5
Title Essential Statistical Inference (EB) : Theory and Methods / / by Dennis D. Boos, L. A. Stefanski.
Author Boos, Dennis D.
Added Personal Name Stefanski, L. A.
Other name(s) SpringerLink (Online service)
Publication New York, NY : : Springer New York :
: Imprint: Springer, , 2013.
Physical Details XVII, 568 p. 34 illus. : online resource.
Series Springer Texts in Statistics 1431-875X ; ; 120
ISBN 9781461448181
Summary Note 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. :
Contents note Roles of Modeling in Statistical Inference.- Likelihood Construction and Estimation.- Likelihood-Based Tests and Confidence Regions.- Bayesian Inference.- Large Sample Theory: The Basics.- Large Sample Results for Likelihood-Based Methods.- M-Estimation (Estimating Equations).- Hypothesis Tests under Misspecification and Relaxed Assumptions .- Monte Carlo Simulation Studies .- Jackknife.- Bootstrap.- Permutation and Rank Tests.- Appendix: Derivative Notation and Formulas.- References.- Author Index.- Example Index -- R-code Index -- Subject Index. .
System details note Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users).
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