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Sinopsis
Excerpt from A Convergence Theorem for Extreme Values From Gaussian Sequences
A double exponential limit law is known to hold for Mn if the random variables Xi are mutually independent, that is rn Q 0, n 0. Berman [2.
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Reseña del editor
Excerpt from A Convergence Theorem for Extreme Values From Gaussian Sequences
A double exponential limit law is known to hold for Mn if the random variables Xi are mutually independent, that is rn Q 0, n 0. Berman [2.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Reseña del editor
Excerpt from A Convergence Theorem for Extreme Values From Gaussian Sequences
The limit law is the same as that which occurs in the independent case. Condition is weaker than but we imposed the strong-mixing condition. In many cases strong-mixing is difficult to verify and it is natural in view of Berman's work to see if the weak convergence results mentioned above hold when the strong-mixing assumption is dropped and just or is assumed. The purpose of this paper is to show that this is, in fact, true. The reader is referred to [7] for some examples of why it is of interest to consider the joint distribution of Mn and Sn.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
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A Convergence Theorem for Extreme Values From Gaussian Sequences
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Paperback. Condición: New. Print on Demand. This book presents a mathematical analysis of certain statistical phenomena that arise when examining extreme values, specifically focusing on values drawn from Gaussian processes. The author demonstrates that the limiting behavior of the maximum and second largest random variables in these sequences exhibits a pattern that involves a double exponential distribution. Furthermore, this limit law holds true under weaker conditions than previously thought, relaxing the requirement for strong mixing assumptions. The book explores conditions under which joint convergence of the maximum and second largest random variables occurs, expanding upon existing theorems in the field. The results presented are applicable to various areas of study involving extreme value theory and Gaussian processes, offering valuable insights into the behavior of these phenomena. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Nº de ref. del artículo: 9781334018343_0
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A Convergence Theorem for Extreme Values from Gaussian Sequences (Classic Reprint)
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: LW-9781334018343
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A Convergence Theorem for Extreme Values from Gaussian Sequences (Classic Reprint)
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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000. Nº de ref. del artículo: LW-9781334018343
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