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

Cristian Meza, Felipe Osorio, Rolando De la Cruz

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


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.

Original languageEnglish
Pages (from-to)121-139
Number of pages19
JournalStatistics and Computing
Issue number1
StatePublished - Jan 2012


  • Mixed-effects model
  • Outliers
  • Random effects
  • SAEM algorithm
  • Scale mixtures of normal distributions


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