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Parameter estimation and the variable selection are two pioneer issues inregression analysis. While traditional variable selection methods require priorestimation of the model parameters, the penalized methods simultaneously carryon parameter estimation and variable select. Therefore, penalized variableselection methods are of great interest and have been extensively studied inliterature. However, most of the papers in literature are only limited to theregression models with uncorrelated error terms and normality assumption. Inthis study, we combine the parameter estimation and the variable selection inregression models with autoregressive error term by using different penaltyfunctions under heavy tailed error distribution assumption. We conduct asimulation study and a real data example to show the performance of theestimators.

Variable Selection in Regression Model with AR(p) Error Terms Based on Heavy Tailed Distributions