1968/02/01 by Sidney J. Yakowitz, Sidney Yakowitz, John D. Spragins +1 · 399 citations
Computer Science · Mathematics · #Applied mathematics #Bayesian Methods and Mixture Models #Binomial (polynomial) #Cauchy distribution #Combinatorics #Exponential family #Finite field #Finite set #Gaussian #Identifiability #Mathematical analysis #Mathematics #Natural exponential family #Statistics
paper · pdf · doi:10.1214/aoms/1177698520
published in The Annals of Mathematical Statistics 39(1), 209-214 (Institute of Mathematical Statistics)
openalex publication_date 1968/02/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26
H. Teicher [5] has initiated a valuable study of the identifiability of finite mixtures (these terms to be defined in the next section), revealing a sufficiency condition that a class of finite mixtures be identifiable and from this, establishing the identifiability of all finite mixtures of one-dimensional Gaussian distributions and all finite mixtures of gamma distributions. From other considerations, he has generalized [4] a result of Feller [1] that arbitrary (and hence finite) mixtures of Poisson distributions are identifiable, and has also shown binomial and uniform families do not generate identifiable mixtures. In this paper it is proven that a family \mathscrF of cumulative distribution functions (cdf's) induces identifiable finite mixtures if and only if \mathscrF is linearly independent in its span over the field of real numbers. Also we demonstrate that finite mixtures of \mathscrF are identifiable if \mathscrF is any of the following: the family of n products of exponential distributions, the multivariate Gaussian family, the union of the last two families, the family of one-dimensional Cauchy distributions, and the non-degenerate members of the family of one-dimensional negative binomial distributions. Finally it is shown that the translation-parameter family generated by any one-dimensional cdf yields identifiable finite mixtures.