Title of article :
Comparing regression methods with non-Gaussian stable errors
Author/Authors :
Alizadeh Noughabi, Reza Department of Mathematics and Computer Science - Amirkabir University of Technology (Tehran Polytechnic) - Tehran, Iran , Mohammadpour, Adel Department of Mathematics and Computer Science - Amirkabir University of Technology (Tehran Polytechnic) - Tehran, Iran
Abstract :
Nolan and Ojeda-Revah (2013) proposed a regression model with heavy-tailed stable errors. In this paper we extend this method for multivariate heavy-tailed errors. Furthermore, A likelihood ratio test (LRT) for testing significant of regression coefficients is proposed. Also, confidence intervals based on Fisher information for Nolan and Ojeda-Revah (2013) method, called NOR, and LRT are computed and compared with well-known methods. At the end we provide some guidance for various error distributions in heavy-tailed cases.
Keywords :
Regression , Quantile regression , Stable distribution , Ordinary least squares , Maximum likelihood
Journal title :
AUT Journal of Mathematics and Computing
