Comparing regression methods with non-Gaussian stable errors

Document Type : Original Article

Authors

Department of Statistics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

Abstract

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.

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References

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AUT Journal of Mathematics and Computing
Volume 3, Issue 1
February 2022
Pages 77-91

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