Comparing regression methods with non-Gaussian stable errors

	Comparing regression methods with non-Gaussian stable errors
AUT Journal of Mathematics and Computing
مقاله 9، دوره 3، شماره 1، اردیبهشت 2022، صفحه 77-91 اصل مقاله (2.26 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.22060/ajmc.2021.20246.1062
نویسندگان
Reza Alizadeh Noughabi؛ Adel Mohammadpour^*
Department of Statistics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
چکیده
Nolan and Ojeda-Revah in [16] 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 [16] method, called NOR, and LRT are computed and compared with well-known methods. In the end, we provide some guidance for various error distributions in heavy-tailed caese.
کلیدواژه‌ها
Regression؛ Quantile regression, Stable distribution؛ Ordinary least squares, Maximum likelihood
مراجع

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