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• W2161849499 abstract "Suppose independent observations Xi, i=1,...,n are observed from a mixture model f(x;Q)[email protected]!f(x;@l)dQ(@l), where @l is a scalar and Q(@l) is a nondegenerate distribution with an unspecified form. We consider to estimate Q(@l) by nonparametric maximum likelihood (NPML) method under two scenarios: (1) the likelihood is penalized by a functional g(Q); and (2) Q is under a constraint g(Q)=g0. We propose a simple and reliable algorithm termed VDM/ECM for Q-estimation when the likelihood is penalized by a linear functional. We show this algorithm can be applied to a more general situation where the penalty is not linear, but a function of linear functionals by a linearization procedure. The constrained NPMLE can be found by penalizing the quadratic distance |g(Q)-g0|^2 under a large penalty factor @c>0 using this algorithm. The algorithm is illustrated with two real data sets." @default.
• W2161849499 created "2016-06-24" @default.
• W2161849499 creator A5039136616 @default.
• W2161849499 date "2007-03-01" @default.
• W2161849499 modified "2023-09-26" @default.
• W2161849499 title "A linearization procedure and a VDM/ECM algorithm for penalized and constrained nonparametric maximum likelihood estimation for mixture models" @default.
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• W2161849499 doi "https://doi.org/10.1016/j.csda.2006.11.033" @default.
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