期刊
JOURNAL OF NONPARAMETRIC STATISTICS
卷 24, 期 1, 页码 19-38出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/10485252.2011.608430
关键词
density estimation; EM algorithm; finite mixture model; identifiability
资金
- NSF [SES-0518772]
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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