Browsing by Author "Liu, JS"

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    Monte Carlo EM with importance reweighting and its applications in random effects models
    (ELSEVIER SCIENCE BV, 1999) Quintana, FA; Liu, JS; del Pino, GE
    In this paper we propose a new Monte Carlo EM algorithm to compute maximum likelihood estimates in the context of random effects models. The algorithm involves the construction of efficient sampling distributions for the Monte Carlo implementation of the E-step, together with a reweighting procedure that allows repeatedly using a same sample of random effects. In addition, we explore the use of stochastic approximations to speed up convergence once stability has been reached. Our algorithm is compared with that of McCulloch (1997). Extensions to more general problems are discussed. (C) 1999 Elsevier Science B.V. All rights reserved.