ON IDENTIFIABILITY OF MIXTURES OF INDEPENDENT DISTRIBUTION LAWS, .
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.
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- 4905 Statistics
- 4901 Applied mathematics
- 0104 Statistics
- 0102 Applied Mathematics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Start / End Page
Related Subject Headings
- 4905 Statistics
- 4901 Applied mathematics
- 0104 Statistics
- 0102 Applied Mathematics