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About Self-Normalized Processes: Limit Theory and Statistical Applications (Probability and its 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 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.
Detailed Information
| Author: | Victor H. de la Peña, Tze Leung Lai, Qi-Man Shao |
|---|---|
| Publication Year: | 2009 |
| ISBN: | 9783540856368 |
| Pages: | 270 |
| Language: | English |
| File Size: | 2.828 |
| Format: | |
| Price: | FREE |
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