Essential Statistical Inference : Theory and Methods 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. . 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.