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 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.
283 pages; ISBN 9783540856368
, or download in
Title: Self-Normalized Processes
Author: Victor H. Peña; Lai Tze Leung; Qi-Man Shao
Measurement in Medicine 2011 US$ 44.00 350 pages
Probabilistic Forecasting and Bayesian Data Assimilation 2015 US$ 44.00 310 pages
- Academic > Mathematics > Probabilities. Mathematical statistics > Probabilities
- Academic > Mathematics > Probabilities. Mathematical statistics > Distribution (Probability theory)
- Academic > Mathematics > Probabilities. Mathematical statistics > Large deviations
- Academic > Mathematics > General
- Mathematics > Probability & Statistics