Sub-Independence: A Useful Concept
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Description
The concept of sub-independence is defined in terms of the convolution of the distributions of random variables, providing a stronger sense of dissociation between random variables than that of uncorrelatedness. If statistical tests reject independence but not lack of correlation, a model with sub-independent components can be appropriate to determine the distribution of the sum of the random variables. This monograph presents most of the important classical results in probability and statistics based on the concept of sub-independence. This concept is much weaker than that of independence and yet can replace independence in most limit theorems as well as well-known results in probability and statistics. This monograph, the first of its kind on the concept of sub-independence, should appeal to researchers in applied sciences where the lack of independence of the uncorrelated random variables may be apparent but the distribution of their sum may not be tractable.
ISBN
978-1-63463-476-2
Publication Date
2015
Publisher
Nova Science Publishers
City
New York
Keywords
Random Variables, Distribution, Probability Theory
Disciplines
Mathematics | Probability | Statistical Theory
Comments
Table of Contents
Chapter 1. Introduction
Chapter 2. Some Results on Characterizations Based on the Concept of Sub-Independence
Chapter 3. Central Limit Theorem and Equivalence of Sub-Independence
References
Index