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Essential Statistical Infer...
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.
Monografía
monografia Rebiun26765987 https://catalogo.rebiun.org/rebiun/record/Rebiun26765987 cr nn 008mamaa 130217s2013 xxu| s |||| 0|eng d 9781461448181 10.1007/978-1-4614-4818-1 doi UPVA 997151648903706 UAM 991007716137604211 CBUC 991004950159206711 CBUC 991001015314106718 CBUC 991003547838306714 CBUC 991000726377006712 CBUC 991010407529906709 CUNEF 991000429608608131 CBUC 991031656339706706 CBUC 991000501679706712 PBT bicssc MAT029000 bisacsh Boos, Dennis D. author Essential Statistical Inference Recurso electronico] Theory and Methods by Dennis D Boos, L. A Stefanski New York, NY Springer New York Imprint: Springer 2013 New York, NY New York, NY Springer New York Imprint: Springer XVII, 568 p. 34 illus. online resource XVII, 568 p. 34 illus. Text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Texts in Statistics 1431-875X 120 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 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. Statistics Statistics Statistical Theory and Methods Statistics, general Statistics and Computing/Statistics Programs Stefanski, L. A. author SpringerLink Book Series (Online Service) Springer Texts in Statistics 1431-875X 120