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

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  • Edward H. Ip

    (University of Southern California)

Abstract

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.

Suggested Citation

  • Edward H. Ip, 2002. "On Single Versus Multiple Imputation for a Class of Stochastic Algorithms Estimating Maximum Likelihood," Computational Statistics, Springer, vol. 17(4), pages 517-524, December.
    • Handle: RePEc:spr:compst:v:17:y:2002:i:4:d:10.1007_s001800200124
    DOI: 10.1007/s001800200124
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    References listed on IDEAS

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    1. Ruud, Paul A., 1991. "Extensions of estimation methods using the EM algorithm," Journal of Econometrics, Elsevier, vol. 49(3), pages 305-341, September.
    2. Tweedie, Richard L., 1975. "Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space," Stochastic Processes and their Applications, Elsevier, vol. 3(4), pages 385-403, October.
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    Cited by:

    1. Siliang Zhang & Yunxiao Chen, 2022. "Computation for Latent Variable Model Estimation: A Unified Stochastic Proximal Framework," Psychometrika, Springer;The Psychometric Society, vol. 87(4), pages 1473-1502, December.
    2. Zhang, Siliang & Chen, Yunxiao, 2022. "Computation for latent variable model estimation: a unified stochastic proximal framework," LSE Research Online Documents on Economics 114489, London School of Economics and Political Science, LSE Library.

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