Self-normalized processes : limit theory and statistical applications
Self-normalized processes are of common occurrence in probabilistic and statistical studies. A prototypical example is Student's t-statistic introduced in 1908 by Gosset, whose portrait is on the front cover. Due to the highly non-linear nature of these processes, the theory experienced a long...
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| Hlavní autoři: | , , |
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| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Vydání: | 1st ed. 2009. |
| Edice: | Probability and Its Applications
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| On-line přístup: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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Description d'après consultation du 30 mars 2012 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Self-normalized processes, limit theory and statistical applications, Victor H. de la Peña, Tze Leung Lai, Qi-Man Shao, 2009, Berlin, Springer, 1 vol. (XIII-275 p.), Probability and its applications, 978-3-540-85635-1 • Self-Normalized Processes, Texte imprimé, 9783642099267 • Self-Normalized Processes, Texte imprimé, 9783540856764 • Self-normalized processes, limit theory and statistical applications, Victor H. de la Peña, Tze Leung Lai, Qi-Man Shao, 2009, Berlin, Springer, 1 vol. (XIII-275 p.), Probability and its applications, 978-3-540-85635-1 • Self-Normalized Processes, Texte imprimé, 9783642099267 • Self-Normalized Processes, Texte imprimé, 9783540856764 • Self-normalized processes, limit theory and statistical applications, Victor H. de la Peña, Tze Leung Lai, Qi-Man Shao, 2009, Berlin, Springer, 1 vol. (XIII-275 p.), Probability and its applications, 978-3-540-85635-1 |
Obsah:
- Independent Random Variables Classical Limit Theorems, Inequalities and Other Tools Self-Normalized Large Deviations Weak Convergence of Self-Normalized Sums Stein's Method and Self-Normalized Berry Esseen Inequality Self-Normalized Moderate Deviations and Laws of the Iterated Logarithm Cramér-Type Moderate Deviations for Self-Normalized Sums Self-Normalized Empirical Processes and U-Statistics Martingales and Dependent Random Vectors Martingale Inequalities and Related Tools A General Framework for Self-Normalization Pseudo-Maximization via Method of Mixtures Moment and Exponential Inequalities for Self-Normalized Processes Laws of the Iterated Logarithm for Self-Normalized Processes Multivariate Self-Normalized Processes with Matrix Normalization Statistical Applications The t-Statistic and Studentized Statistics Self-Normalization for Approximate Pivots in Bootstrapping Pseudo-Maximization in Likelihood and Bayesian Inference Sequential Analysis and Boundary Crossing Probabilities for Self-Normalized Statistics