A parameter expansion version of the SAEM algorithm

Lavielle, Marc; Meza, Cristian

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.

Más información

Título según WOS: ID WOS:000247169400004 Not found in local WOS DB
Título de la Revista: STATISTICS AND COMPUTING
Volumen: 17
Número: 2
Editorial: Springer
Fecha de publicación: 2007
Página de inicio: 121
Página final: 130
DOI:

10.1007/s11222-006-9007-6

Notas: ISI