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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 th...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2013.
|
| Edición: | 1st ed. 2013. |
| Colección: | Springer Texts in Statistics,
120 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Boos, Dennis D. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Essential Statistical Inference |h [electronic resource] : |b Theory and Methods / |c by Dennis D. Boos, L A Stefanski. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2013. | |
| 300 | |a XVII, 568 p. 34 illus. |b online resource. | ||
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| 490 | 1 | |a Springer Texts in Statistics, |x 2197-4136 ; |v 120 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. . | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Mathematical statistics-Data processing. | |
| 650 | 1 | 4 | |a Statistical Theory and Methods. |
| 650 | 2 | 4 | |a Statistics. |
| 650 | 2 | 4 | |a Statistics and Computing. |
| 700 | 1 | |a Stefanski, L A. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 776 | 0 | 8 | |i Printed edition: |z 9781489987938 |
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| 830 | 0 | |a Springer Texts in Statistics, |x 2197-4136 ; |v 120 | |
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