NDLI: A parameter expansion version of the SAEM algorithm

Content Provider	Springer Nature Link
Author	Lavielle, Marc Meza, Cristian
Copyright Year	2007
Abstract	The EM algorithm and its extensions are very popular tools for maximum likelihood estimation in incomplete data setting. However, one of the limitations of these methods is their slow convergence. The PX-EM (parameter-expanded EM) algorithm was proposed by Liu, Rubin and Wu to make EM much faster. On the other hand, stochastic versions of EM are powerful alternatives of EM when the E-step is untractable in a closed form. In this paper we propose the PX-SAEM which is a parameter expansion version of the so-called SAEM (Stochastic Approximation version of EM). PX-SAEM is shown to accelerate SAEM and improve convergence toward the maximum likelihood estimate in a parametric framework. Numerical examples illustrate the behavior of PX-SAEM in linear and nonlinear mixed effects models.
Starting Page	121
Ending Page	130
Page Count	10
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	17
Issue Number	2
e-ISSN	15731375
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2007-01-30
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	EM PX-EM SAEM Nonlinear mixed effects models Artificial Intelligence (incl. Robotics) Mathematics Numeric Computing Statistics Statistics and Computing/Statistics Programs
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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