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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.
| Autorius: | Victor H. Peña, Qi-Man Shao, Tze Leung Lai, |
| Serija: | Probability and Its Applications |
| Leidėjas: | Springer Berlin Heidelberg |
| Išleidimo metai: | 2010 |
| Knygos puslapių skaičius: | 292 |
| ISBN-10: | 3642099262 |
| ISBN-13: | 9783642099267 |
| Formatas: | 235 x 155 x 16 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „Self-Normalized Processes: Limit Theory and Statistical Applications“
