ON IDENTIFIABILITY OF MIXTURES OF INDEPENDENT DISTRIBUTION LAWS, .

Published

Journal Article

We consider representations of a joint distribution law of a family of categorical random variables (i.e., a multivariate categorical variable) as a mixture of independent distribution laws (i.e. distribution laws according to which random variables are mutually independent). For infinite families of random variables, we describe a class of mixtures with identifiable mixing measure. This class is interesting from a practical point of view as well, as its structure clarifies principles of selecting a "good" finite family of random variables to be used in applied research. For finite families of random variables, the mixing measure is never identifiable; however, it always possesses a number of identifiable invariants, which provide substantial information regarding the distribution under consideration.

Full Text

Duke Authors

Cited Authors

  • Kovtun, M; Akushevich, I; Yashin, A

Published Date

  • January 2014

Published In

Volume / Issue

  • 18 /

Start / End Page

  • 207 - 232

PubMed ID

  • 25705110

Pubmed Central ID

  • 25705110

Electronic International Standard Serial Number (EISSN)

  • 1262-3318

International Standard Serial Number (ISSN)

  • 1292-8100

Digital Object Identifier (DOI)

  • 10.1051/ps/2011166

Language

  • eng