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Peña / Shao / Lai

Self-Normalized Processes

Limit Theory and Statistical Applications

Medium: Buch
ISBN: 978-3-642-09926-7
Verlag: Springer
Erscheinungstermin: 30.11.2010
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9783642099267
  • Medium: Buch
  • ISBN: 978-3-642-09926-7
  • Verlag: Springer
  • Erscheinungstermin: 30.11.2010
  • Sprache(n): Englisch
  • Auflage: 1. Auflage. Softcover version of original hardcover Auflage 2009
  • Serie: Probability and Its Applications
  • Produktform: Kartoniert, Previously published in hardcover
  • Gewicht: 446 g
  • Seiten: 275
  • Format (B x H x T): 155 x 235 x 16 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Autoren

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.