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On Single Versus Multiple Imputation for a Class of Stochastic Algorithms Estimating Maximum Likelihood

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Summary

We discuss a special class of stochastic versions of the EM algorithms. The advantage of the single imputation procedure in non-exponential family applications is highlighted. We prove that ergodic properties of the stochastic algorithms are dependent not on the multiplicity of the imputation scheme but rather on the stability of the deterministic component of an underlying stochastic difference equation.

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Acknowledgement

This paper is a modified version of a chapter from the author’s doctoral dissertation. The author thanks both Jean Diebolt and Ingram Olkin for their guidance, and for suggestions to this paper.

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  1. Marshall School of Business, University of Southern California, Los Angeles, CA, 90089, USA

    Edward H. Ip

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  1. Edward H. Ip
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Ip, E.H. On Single Versus Multiple Imputation for a Class of Stochastic Algorithms Estimating Maximum Likelihood. Computational Statistics 17, 517–524 (2002). https://doi.org/10.1007/s001800200124

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