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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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Peña, Victor H. (Autor), Lai, Tze Leung (Autor), Shao, Qi-Man (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2009.
Edición:1st ed. 2009.
Colección:Probability and Its Applications
Temas:
Acceso en línea:Texto Completo

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245 1 0 |a Self-Normalized Processes  |h [electronic resource] :  |b Limit Theory and Statistical Applications /  |c by Victor H. Peña, Tze Leung Lai, Qi-Man Shao. 
250 |a 1st ed. 2009. 
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490 1 |a Probability and Its Applications 
505 0 |a 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. 
520 |a 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 period of slow development. In recent years there have been a number of important advances in the theory and applications of self-normalized processes. Some of these developments are closely linked to the study of central limit theorems, which imply that self-normalized processes are approximate pivots for statistical inference. The present volume covers recent developments in the area, including self-normalized large and moderate deviations, and laws of the iterated logarithms for self-normalized martingales. This is the first book that systematically treats the theory and applications of self-normalization. 
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650 0 |a Statistics . 
650 1 4 |a Probability Theory. 
650 2 4 |a Statistical Theory and Methods. 
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