On Asymmetric Regression Models with Allowance for Temporal Dependence

Abstract

Log-symmetric regression models are particularly useful when the response variable is continuous, strictly positive and asymmetric. In this paper, we proposed a class of log-symmetric regression models in the context of correlated errors. The proposed models provide a novel alternative to the existing log-symmetric regression models due to its flexibility in accommodating correlation. We discuss some properties, parameter estimation by the conditional maximum likelihood method and goodness of fit of the proposed model. We also provide expressions for the observed Fisher information matrix. Two Monte Carlo simulation studies are presented to evaluate the performance of the conditional maximum likelihood estimators and two types of residuals. Finally, a full analysis of a real-world environmental data set is presented to illustrate the proposed approach.