TY  - BOOK
AU  - Boos,Dennis D.
AU  - Stefanski,L.A.
ED  - SpringerLink (Online service)
TI  - Essential Statistical Inference: Theory and Methods 
T2  - Springer Texts in Statistics,
SN  - 9781461448181
AV  - QA276-280 
U1  - 519.5 23
PY  - 2013///
CY  - New York, NY
PB  - Springer New York, Imprint: Springer
KW  - Statistics
KW  - Statistical Theory and Methods
KW  - Statistics, general
KW  - Statistics and Computing/Statistics Programs
N1  - 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
N2  - 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. 
UR  - http://dx.doi.org/10.1007/978-1-4614-4818-1
ER  -