articleTop 10% cited
Finding the Observed Information Matrix When Using the <i>EM</i> Algorithm
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1982 · Vol. 44(2) · pp. 226–233
Thomas A. Louis✉(Harvard University)
Abstract
Summary A procedure is derived for extracting the observed information matrix when the EM algorithm is used to find maximum likelihood estimates in incomplete data problems. The technique requires computation of a complete-data gradient vector or second derivative matrix, but not those associated with the incomplete data likelihood. In addition, a method useful in speeding up the convergence of the EM algorithm is developed. Two examples are presented.
Neural Networks and ApplicationsAdvanced Statistical Methods and ModelsBlind Source Separation TechniquesAlgorithmMatrix (chemical analysis)Computer scienceMaterials science
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2,218
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References
Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1977 · 49,286 citations
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