Journal Article10.1007/S001800000031
The S-U algorithm for missing data problems
Glen A. Satten,Somnath Datta +1 more
12
TL;DR: In this paper, a Monte-Carlo method for finding the solution of an estimating equation that can be expressed as the expected value of a "full data" estimating equation in which the expected values is with respect to the distribution of the missing data given the observed data was presented.
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Abstract: We present a new Monte-Carlo method for finding the solution of an estimating equation that can be expressed as the expected value of a ‘full data’ estimating equation in which the expected value is with respect to the distribution of the missing data given the observed data. Equations such as these arise whenever the E-M algorithm can be used. The algorithm alternates between two steps: an S-step, in which the missing data are simulated, either from the conditional distribution described above or from a more convenient importance sampling distribution, and a U-step, in which parameters are updated using a closed-form expression that does not require a numerical maximization. We present two numerical examples to illustrate the method. Theoretical results are obtained establishing consistency and asymptotic normality of the approximate solution obtained by our method.
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The large sample properties of the solutions of general estimating equations
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