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Direct Calculation of the Variance of Maximum Penalized Likelihood Estimates via EM Algorithm.

The variance of the maximum penalized likelihood estimate obtained through the EM algorithm has not been explored in detail. We provide a simple and intuitive new representation for the variance that can be computed from the EM algorithm directly. For pedagogical purposes, we illustrate the new form...

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Published in:American Statistician Vol. 68; no. 2; pp. 93 - 98
Main Authors: Lee, Woojoo, Pawitan, Yudi
Format: Article
Published: Taylor & Francis Ltd May2014
Subjects:
Maximum likelihood statistics
Expectation-maximization algorithms
Newton-Raphson method
Derivatives (Mathematics)
Multinomial distribution
Online Access:View this record in EBSCOhost
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        10.1080/00031305.2014.899273
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        atl: Direct Calculation of the Variance of Maximum Penalized Likelihood Estimates via EM Algorithm.
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          Lee, Woojoo
          Pawitan, Yudi
      su:
        Maximum likelihood statistics
        Expectation-maximization algorithms
        Newton-Raphson method
        Derivatives (Mathematics)
        Multinomial distribution
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        subj:
          Maximum likelihood statistics
          Expectation-maximization algorithms
          Newton-Raphson method
          Derivatives (Mathematics)
          Multinomial distribution
      keyword:
        Expectation-maximization algorithm
        Observed information
        Standard errors
        Expectation-maximization algorithm
        Observed information
        Standard errors
      ab: The variance of the maximum penalized likelihood estimate obtained through the EM algorithm has not been explored in detail. We provide a simple and intuitive new representation for the variance that can be computed from the EM algorithm directly. For pedagogical purposes, we illustrate the new formula with two examples where analytical solutions are possible.
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      doctype: Article
      src: R
    language: English
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