Abstract
Optimization via simulation is a promising technique to solve maximum likelihood problems in incomplete data models. Among the techniques proposed to date to solve this problem, the MCEM algorithm proposed by Wei & Tanner (1991) plays a preeminent role. Perhaps surprisingly, very little is known on the convergence of this algorithm and on the strategies to monitor this convergence. A particular emphasis is given on the stability issue (which is not guaranteed in the original proposal by Wei & Tanner, 1991). A random truncation strategy, inspired by Chen’s truncation method for stochastic approximation algorithms, is proposed and analysed.
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© 1998 Springer-Verlag Berlin Heidelberg
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Fort, G., Moulines, E., Soulier, P. (1998). On the Convergence of Iterated Random Maps with Applications to the MCEM Algorithm. In: Payne, R., Green, P. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-662-01131-7_41
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DOI: https://doi.org/10.1007/978-3-662-01131-7_41
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1131-5
Online ISBN: 978-3-662-01131-7
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