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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.

Grenzwertsatz. --- Limit theorems (Probability theory). --- Mathematical statistics. --- t-test (Statistics). --- Limit theorems (Probability theory) --- Mathematical statistics --- t-test (Statistics) --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Probabilities. --- Statistical inference --- Statistics, Mathematical --- Probability --- Statistical methods --- Mathematics. --- Statistics. --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Combinations --- Chance --- Least squares --- Risk --- Statistics --- Probabilities --- Sampling (Statistics) --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics