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Estimation in nonlinear mixed-effects models using heavy-tailed distributions

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Autor Meza C.
Autor Osorio F.
Autor de la Cruz R.
Fecha Tésis 2012
Fecha Ingreso 2014-04-04T17:21:54Z
Fecha Disponible 2014-04-04T17:21:54Z
Fecha en Repositorio 2014-04-04
dc.description.abstract Nonlinear mixed-effects models are very useful to analyze repeated measures data and are used in a variety of applications. Normal distributions for random effects and residual errors are usually assumed, but such assumptions make inferences vulnerable to the presence of outliers. In this work, we introduce an extension of a normal nonlinear mixed-effects model considering a subclass of elliptical contoured distributions for both random effects and residual errors. This elliptical subclass, the scale mixtures of normal (SMN) distributions, includes heavy-tailed multivariate distributions, such as Student-t, the contaminated normal and slash, among others, and represents an interesting alternative to outliers accommodation maintaining the elegance and simplicity of the maximum likelihood theory. We propose an exact estimation procedure to obtain the maximum likelihood estimates of the fixed-effects and variance components, using a stochastic approximation of the EM algorithm. We compare the performance of the normal and the SMN models with two real data sets. © 2010 Springer Science+Business Media, LLC. en_US
dc.source Statistics and Computing
Link Descarga dc.source.uri
Link Descarga dc.source.uri
Title dc.title Estimation in nonlinear mixed-effects models using heavy-tailed distributions en_US
dc.description.keywords Mixed-effects model; Outliers; Random effects; SAEM algorithm; Scale mixtures of normal distributions en_US

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