Palestrante: Camila Borelli Zeller (UFJF) Data: 08/10 Hora: 15:30
Local: Laboratório de Sistemas Estocásticos (LSE), Sala I-044-B, Centro de Tecnologia - UFRJ.
Título: Finite mixture of regression models based on multivariate scale
mixtures of skew-normal distributions.Resumo:
The traditional estimation of mixture regression models is based on the assumption of
component normality (or symmetry), making it sensitive to outliers, heavy-tailed errors,
and asymmetric errors. In this work, we propose addressing these issues simultaneously
by considering a finite mixture of regression models with multivariate scale mixtures of
skew-normal distributions. This approach provides greater flexibility in modeling data,
accommodating both skewness and heavy tails. Additionally, the proposed model
allows the use of a specific vector of regressors for each dependent variable. The main
advantage of using the mixture of regression models under the class of multivariate
scale mixtures of skew-normal distributions is their convenient hierarchical
representation, which allows easy implementation of inference. We develop a simple
expectation–maximization (EM) type algorithm to perform maximum likelihood
inference for the parameters of the proposed model. The observed information matrix is
derived analytically to calculate standard errors. Some simulation studies are also
presented to examine the robustness of this flexible model against outlying
observations. Finally, a real dataset is analyzed, demonstrating the practical value of the
proposed method. The R scripts implementing our methods are available on the GitHub
repository at https://bit.ly/3CLcI1W.
component normality (or symmetry), making it sensitive to outliers, heavy-tailed errors,
and asymmetric errors. In this work, we propose addressing these issues simultaneously
by considering a finite mixture of regression models with multivariate scale mixtures of
skew-normal distributions. This approach provides greater flexibility in modeling data,
accommodating both skewness and heavy tails. Additionally, the proposed model
allows the use of a specific vector of regressors for each dependent variable. The main
advantage of using the mixture of regression models under the class of multivariate
scale mixtures of skew-normal distributions is their convenient hierarchical
representation, which allows easy implementation of inference. We develop a simple
expectation–maximization (EM) type algorithm to perform maximum likelihood
inference for the parameters of the proposed model. The observed information matrix is
derived analytically to calculate standard errors. Some simulation studies are also
presented to examine the robustness of this flexible model against outlying
observations. Finally, a real dataset is analyzed, demonstrating the practical value of the
proposed method. The R scripts implementing our methods are available on the GitHub
repository at https://bit.ly/3CLcI1W.
BENITES, L.; LACHOS, V. H.; BOLFARINE, H.; ZELLER, CAMILA BORELLI.
Finite mixture of regression models based on multivariate scale mixtures of skew-
normal distributions. COMPUTATIONAL STATISTICS, p. 1-32, 2025.
Finite mixture of regression models based on multivariate scale mixtures of skew-
normal distributions. COMPUTATIONAL STATISTICS, p. 1-32, 2025.
