Journal
JOURNAL OF NONPARAMETRIC STATISTICS
Volume 24, Issue 1, Pages 19-38Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/10485252.2011.608430
Keywords
density estimation; EM algorithm; finite mixture model; identifiability
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Funding
- NSF [SES-0518772]
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We present an algorithm for estimating parameters in a mixture-of-regressions model in which the errors are assumed to be independent and identically distributed but no other assumption is made. This model is introduced as one of several recent generalizations of the standard fully parametric mixture of linear regressions in the literature. A sufficient condition for the identifiability of the parameters is stated and proved. Several different versions of the algorithm, including one that has a provable ascent property, are introduced. Numerical tests indicate the effectiveness of some of these algorithms.
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