Some Aspects of Inference for Multivariate Infinitely Divisible Distributions.

Abstract

Measurement of dependence in the infinitely divisible class of multivariate distributions, based on developments in probability theory for that class, is discussed. It has been shown that pairwise independence is equivalent to mutual independence in the infinitely divisible class. When the infinitely divisible variables contain no normal component (in particular, when they are discrete), the cumulant of order (2,2) can be used as a measure of pairwise dependence; when a normal component is present, the appropriate measure of pairwise dependence also involves the covariance. Results for testing independence of infinitely divisible random variables are discussed. A method of testing normality against infinitely divisible alternatives is given. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jun 15, 1982
Accession Number
ADA116582

Entities

People

  • Stanley L. Sclove

Organizations

  • University of Illinois at Chicago

Tags

Communities of Interest

  • Air Platforms
  • Biomedical

DTIC Thesaurus Topics

  • Business Administration
  • Coefficients
  • Covariance
  • Data Science
  • Illinois
  • Information Science
  • Integrals
  • Mathematics
  • Measurement
  • Military Research
  • Normal Distribution
  • Normality
  • Observation
  • Probability
  • Random Variables
  • Statistical Inference
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference