Overview: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.

This book is on page /88f0ff300130a9332bf7ee8997e69000/book/1603729687-9783642099267. It was written by the following authors: Qi-Man Shao, Tze Leung Lai, Victor H. Pena. Book Self-Normalized Processes: Limit Theory and Statistical Applications - Probability and Its Applications, which can be read online, published by the company: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG. Other books on similar topics can be found in sections: Medicine, Science, Technology. The book was published on 2010-11-30 00:00:00. It has 275 pages and is published in Paperback format and weight 450 g. File for download Self-Normalized Processes: Limit Theory and Statistical Applications - Probability and Its Applications has PDF format and is called self-normalized-processes-limit-theory-and-statistical-applications-probability-and-its-applications.pdf. Other books you can download below. Our nottedeiricercatoriaq.it site is not responsible for the content of PDF files.


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