NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Print
CONTENTS NOTE
Text of Note
This book is for students and researchers who have had a first year graduate level mathematicalstatistics 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 numberof examples and problems. An important goal has been to make the topics accessible to a wide audience, with little overt relianceon measure theory. A typical semester course consists of Chapters 1-6 (likelihood-based estimationand 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 forresearch concentrated on measurement error, including a co-authored book on non-linear measurementerror models. In recent years the authors have jointly worked on variable selection methods.
Text of 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 RelaxedAssumptions .-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..?╗╣