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...
Sábháilte in:
| Príomhchruthaitheoirí: | , |
|---|---|
| Formáid: | Livre numérique |
| Teanga: | Anglais |
| Foilsithe / Cruthaithe: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Eagrán: | 1st ed. 2013. |
| Sraith: | Springer Texts in Statistics
120 |
| Rochtain ar líne: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nóta: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Essential statistical inference, theory and methods, Dennis D. Boos, L.A. Stefanski, 2013, New York, Springer, 1 vol. (XVII-568 p.), Springer texts in statistics, 978-1-4614-4817-4 • Essential Statistical Inference, Texte imprimé, 9781489987938 • Essential statistical inference, theory and methods, Dennis D. Boos, L.A. Stefanski, 2013, New York, Springer, 1 vol. (XVII-568 p.), Springer texts in statistics, 978-1-4614-4817-4 • Essential Statistical Inference, Texte imprimé, 9781461448198 |
Clár na nÁbhar:
- 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.