Journal of Siberian Federal University. Mathematics & Physics / Heavy Tail Index Estimator through Weighted Least-squares Rank Regression

Full text (.pdf)
Issue
Journal of Siberian Federal University. Mathematics & Physics. 2022 15 (6)
Authors
Khemissi, Zahia; Brahimi, Brahim; Benatia, Fatah
Contact information
Khemissi, Zahia: Laboratory of Applied Mathematics Mohamed Khider University Biskra, Algeria; ; Brahimi, Brahim: Laboratory of Applied Mathematics Mohamed Khider University Biskra, Algeria; OCRID: 0000-0003-4482-3749; Benatia, Fatah: Laboratory of Applied Mathematics Mohamed Khider University Biskra, Algeria; OCRID: 00000-0002-3236-8729
Keywords
Frechet distribution; weighted least-squares regression; Rank regression; Monte Carlo simulation; shape parameter
Abstract

In this paper, we proposed a weighted least square estimator based method to estimate the shape parameter of the Frechet distribution. We show the performance of the proposed estimator in a simulation study, it is found that the considered weighted estimation method shows better performance than the maximum likelihood estimation. Maximum product of spacing estimation and least-squares in terms of bias and root mean square error for most of the considered sample sizes. In addition, a real example from Danish data is provided to demonstrate the performance of the considered method

Pages
797–805
DOI
10.17516/1997-1397-2022-15-6-797-805
Paper at repository of SibFU
https://elib.sfu-kras.ru/handle/2311/149661