On identifiability of mixtures of independent distribution laws^{∗,∗∗,∗∗∗}

¹ Duke University, Dept. of Biology, Benfey Lab Durham, 27708 NC, USA
mikhail.kovtun@duke.edu
² Center for Population Health and Aging Durham, 27708 NC, USA
igor.akushevich@duke.edu; anatoly.yashin@duke.edu

Received: 25 April 2010
Revised: 26 March 2011

Abstract

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.