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A stable estimator of the information matrix under EM for dependent data

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Abstract

This article develops a new and stable estimator for information matrix when the EM algorithm is used in maximum likelihood estimation. This estimator is constructed using the smoothed individual complete-data scores that are readily available from running the EM algorithm. The method works for dependent data sets and when the expectation step is an irregular function of the conditioning parameters. In comparison to the approach of Louis (J. R. Stat. Soc., Ser. B 44:226–233, 1982), this new estimator is more stable and easier to implement. Both real and simulated data are used to demonstrate the use of this new estimator.

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Authors and Affiliations

  1. Risk Management Institute and Department of Finance, National University of Singapore, Singapore, Singapore

    Jin-Chuan Duan

  2. ESSEC Business School, Paris, France

    Andras Fulop

Authors
  1. Jin-Chuan Duan
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  2. Andras Fulop
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Correspondence to Andras Fulop.

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A. Fulop is a research fellow at CREST.

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Duan, JC., Fulop, A. A stable estimator of the information matrix under EM for dependent data. Stat Comput 21, 83–91 (2011). https://doi.org/10.1007/s11222-009-9149-4

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  • DOI: https://doi.org/10.1007/s11222-009-9149-4

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