Two Taylor-series approximation methods for nonlinear mixed models
Computational Statistics & Data Analysis
The mean-shift outlier model in general weighted regression and its applications
Computational Statistics & Data Analysis
A general class of multivariate skew-elliptical distributions
Journal of Multivariate Analysis
Simulation of right and left truncated gamma distributions by mixtures
Statistics and Computing
Mixed Models: Theory and Applications (Wiley Series in Probability and Statistics)
Mixed Models: Theory and Applications (Wiley Series in Probability and Statistics)
EM algorithms for nonlinear mixed effects models
Computational Statistics & Data Analysis
Assessment of local influence in elliptical linear models with longitudinal structure
Computational Statistics & Data Analysis
A parameter expansion version of the SAEM algorithm
Statistics and Computing
Linear mixed models with skew-elliptical distributions: A Bayesian approach
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Estimation in the probit normal model for binary outcomes using the SAEM algorithm
Computational Statistics & Data Analysis
Influence diagnostics in nonlinear mixed-effects elliptical models
Computational Statistics & Data Analysis
Maximum likelihood estimation in nonlinear mixed effects models
Computational Statistics & Data Analysis
Bayesian inference in nonlinear mixed-effects models using normal independent distributions
Computational Statistics & Data Analysis
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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.