On Use of the Em Algorithm for Penalized Likelihood Estimation

SUMMARY The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alterna...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Methodological Methodological, 1990, Vol.52 (3), p.443-452
1. Verfasser:	Green, Peter J.
Format:	Artikel
Sprache:	eng
Schlagworte:	convergence rates em algorithm Exact sciences and technology Mathematics maximum a posteriori estimation Nonparametric inference one‐step‐late algorithm, penalized likelihood Probability and statistics Sciences and techniques of general use Statistics
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Beschreibung
Zusammenfassung:	SUMMARY The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alternative that is usually more practical and converges at least as quickly.
ISSN:	0035-9246 1369-7412 2517-6161 1467-9868
DOI:	10.1111/j.2517-6161.1990.tb01798.x