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

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dc.contributor.authorMeza, Cristian
dc.contributor.authorOsorio, Felipe
dc.contributor.authorDe la Cruz, Rolando
dc.date.accessioned2025-01-20T23:59:53Z
dc.date.available2025-01-20T23:59:53Z
dc.date.issued2012
dc.description.abstractNonlinear 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.
dc.fuente.origenWOS
dc.identifier.doi10.1007/s11222-010-9212-1
dc.identifier.eissn1573-1375
dc.identifier.issn0960-3174
dc.identifier.urihttps://doi.org/10.1007/s11222-010-9212-1
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/95314
dc.identifier.wosidWOS:000297543400009
dc.issue.numero1
dc.language.isoen
dc.pagina.final139
dc.pagina.inicio121
dc.revistaStatistics and computing
dc.rightsacceso restringido
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleEstimation in nonlinear mixed-effects models using heavy-tailed distributions
dc.typeartículo
dc.volumen22
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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