A stable estimator of the information matrix under EM for dependent data
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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.
KeywordsParticle filter EM Information matrix Kalman filter GARCH Maximum likelihood
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- Cappe, O., Moulines, E.: On the use of particle filtering for maximum likelihood parameter estimation. In: European Signal Processing Conference (EUSIPCO), Antalya, Turkey (2005) Google Scholar