# On identifiability of mixtures of independent distribution laws

Mikhail Kovtun; Igor Akushevich; Anatoliy Yashin

ESAIM: Probability and Statistics (2014)

- Volume: 18, page 207-232
- ISSN: 1292-8100

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topKovtun, Mikhail, Akushevich, Igor, and Yashin, Anatoliy. "On identifiability of mixtures of independent distribution laws." ESAIM: Probability and Statistics 18 (2014): 207-232. <http://eudml.org/doc/274398>.

@article{Kovtun2014,

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.},

author = {Kovtun, Mikhail, Akushevich, Igor, Yashin, Anatoliy},

journal = {ESAIM: Probability and Statistics},

keywords = {latent structure analysis; mixed distributions; identifiability; moment problem; mixed distribution},

language = {eng},

pages = {207-232},

publisher = {EDP-Sciences},

title = {On identifiability of mixtures of independent distribution laws},

url = {http://eudml.org/doc/274398},

volume = {18},

year = {2014},

}

TY - JOUR

AU - Kovtun, Mikhail

AU - Akushevich, Igor

AU - Yashin, Anatoliy

TI - On identifiability of mixtures of independent distribution laws

JO - ESAIM: Probability and Statistics

PY - 2014

PB - EDP-Sciences

VL - 18

SP - 207

EP - 232

AB - 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.

LA - eng

KW - latent structure analysis; mixed distributions; identifiability; moment problem; mixed distribution

UR - http://eudml.org/doc/274398

ER -

## References

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