A parameter expansion version of the SAEM algorithm |
| |
Authors: | Marc Lavielle Cristian Meza |
| |
Institution: | (1) INRIA Futurs and Université Paris-Sud, Equipe de Probabilités, Statistique et Modélisation, Bat 425, 91400 Orsay, France;(2) Laboratoire de Mathématiques, Université Paris-Sud, 91405 Orsay Cedex, France |
| |
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. |
| |
Keywords: | EM PX-EM SAEM Nonlinear mixed effects models |
本文献已被 SpringerLink 等数据库收录! |
|