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Asymptotic properties of a stochastic EM algorithm for mixtures with censored data. (English) Zbl 1178.62020
Summary: Weak consistency and asymptotic normality are shown for a stochastic EM algorithm for censored data from a mixture of distributions under lognormal assumptions. The asymptotic properties hold for all parameters of the distributions, including the mixing parameter. In order to make parameter estimation meaningful it is necessary to know that the censored mixture distribution is identifiable. General conditions under which this is the case are given. The stochastic EM algorithm addressed in this paper is used for estimation of wood fibre length distributions based on optically measured data from cylindric wood samples (increment cores).

MSC:
62F12 Asymptotic properties of parametric estimators
65C60 Computational problems in statistics (MSC2010)
62N01 Censored data models
62F10 Point estimation
62P10 Applications of statistics to biology and medical sciences; meta analysis
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