Nonlinear mixed-effects models with scale mixture of skew-normal distributions |
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Authors: | Marcos Antonio Alves Pereira Cibele Maria Russo |
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Affiliation: | 1. Joint Graduate Program in Statistics, USP/UFSCar, S?o Carlos, Brazil;2. Present address: Amílcar Ferreira Sobral Campus, Federal University of Piauí, Floriano, Brazil;3. Department of Applied Mathematics and Statistics, Institute of Mathematical and Computer Sciences, University of S?o Paulo, S?o Carlos, Brazil |
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Abstract: | Aiming to avoid the sensitivity in the parameters estimation due to atypical observations or skewness, we develop asymmetric nonlinear regression models with mixed-effects, which provide alternatives to the use of normal distribution and other symmetric distributions. Nonlinear models with mixed-effects are explored in several areas of knowledge, especially when data are correlated, such as longitudinal data, repeated measures and multilevel data, in particular, for their flexibility in dealing with measures of areas such as economics and pharmacokinetics. The random components of the present model are assumed to follow distributions that belong to scale mixtures of skew-normal (SMSN) distribution family, that encompasses distributions with light and heavy tails, such as skew-normal, skew-Student-t, skew-contaminated normal and skew-slash, as well as symmetrical versions of these distributions. For the parameters estimation we obtain a numerical solution via the EM algorithm and its extensions, and the Newton-Raphson algorithm. An application with pharmacokinetic data shows the superiority of the proposed models, for which the skew-contaminated normal distribution has shown to be the most adequate distribution. A brief simulation study points to good properties of the parameter vector estimators obtained by the maximum likelihood method. |
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Keywords: | Nonlinear model skewness mixed-effects scale mixtures of skew-normal EM algorithm |
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