| Self-Normalized Processes: Limit Theory and Statistical Applications 2009 Edition Contributor(s): Peña, Victor H. (Author), Lai, Tze Leung (Author), Shao, Qi-Man (Author) |
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ISBN: 3540856358 ISBN-13: 9783540856351 Publisher: Springer OUR PRICE: $132.99 Product Type: Hardcover - Other Formats Published: January 2009 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Mathematics | Probability & Statistics - General |
| Dewey: 519.2 |
| LCCN: 2008938080 |
| Series: Probability and Its Applications (Springer) |
| Physical Information: 0.8" H x 6.4" W x 9.4" (1.35 lbs) 275 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: Self-Normalized Processes are very common in statistical and probabilistic studies. Due to the highly non-linear nature of these processes, the theory experienced a period of slow development. In recent years there have been a number of important developments in its theory and applications. Some of their features are closely linked to the study of the central limit theorems, and hence are used as pivots in the estimation of parameters. The present volume covers the recent developments in the area including extensions of Kolmogorov's law of the iterated logarithm for sums of independent variables to the case of Self-Normalized martingales. This is the first book that systematically treats the theory and applications of Self-Normalization. |