Large Sample Techniques for Statistics
This book offers a comprehensive guide to large sample techniques in statistics. More importantly, it focuses on thinking skills rather than just what formulae to use; it provides motivations, and intuition, rather than detailed proofs; it begins with very simple techniques, and connects theory and...
Guardat en:
| Autor principal: | |
|---|---|
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Edició: | 1. |
| Col·lecció: | Springer Texts in Statistics
|
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Large sample techniques for statistics, Jiming Jiang, New York, Springer, 2010, 1 vol. (XVII-609 p.), Springer texts in statistics, 978-1-441-96826-5 • Large sample techniques for statistics, Jiming Jiang, New York, Springer, 2010, 1 vol. (XVII-609 p.), Springer texts in statistics, 978-1-441-96826-5 • Large Sample Techniques for Statistics, Texte imprimé, 9781441968289 • Large Sample Techniques for Statistics, Texte imprimé, 9781461426233 |
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| 100 | 1 | |a Jiang, Jiming. | |
| 245 | 1 | 0 | |a Large Sample Techniques for Statistics |c by Jiming Jiang. |
| 250 | |a 1. | ||
| 260 | |a New York, NY : |b Springer New York. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Springer Texts in Statistics |x 2197-4136 | |
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 1 | |a The ?-? Arguments Modes of Convergence Big O, Small o, and the Unspecified c Asymptotic Expansions Inequalities Sums of Independent Random Variables Empirical Processes Martingales Time and Spatial Series Stochastic Processes Nonparametric Statistics Mixed Effects Models Small-Area Estimation Jackknife and Bootstrap Markov-Chain Monte Carlo | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a This book offers a comprehensive guide to large sample techniques in statistics. More importantly, it focuses on thinking skills rather than just what formulae to use; it provides motivations, and intuition, rather than detailed proofs; it begins with very simple techniques, and connects theory and applications in entertaining ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first 10 chapters contains at least one section of case study. The last five chapters are devoted to special areas of applications. The sections of case studies and chapters of applications fully demonstrate how to use methods developed from large sample theory in various, less-than-textbook situations. The book is supplemented by a large number of exercises, giving the readers plenty of opportunities to practice what they have learned. The book is mostly self-contained with the appendices providing some backgrounds for matrix algebra and mathematical statistics. The book is intended for a wide audience, ranging from senior undergraduate students to researchers with Ph.D. degrees. A first course in mathematical statistics and a course in calculus are prerequisites. Jiming Jiang is a Professor of Statistics at the University of California, Davis. He is a Fellow of the American Statistical Association and a Fellow of the Institute of Mathematical Statistics. He is the author of another Springer book, Linear and Generalized Linear Mixed Models and Their Applications (2007). Jiming Jiang is a prominent researcher in the fields of mixed effects models, small area estimation and model selection. Most of his research papers have involved large sample techniques. He is currently an Associate Editor of the Annals of Statistics | ||
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| 776 | 0 | |t Large Sample Techniques for Statistics |b Texte imprimé |z 9781441968289 | |
| 776 | 0 | |t Large Sample Techniques for Statistics |b Texte imprimé |z 9781461426233 | |
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