Geodesic
Heavy tail index estimator through weighted least-squares rank regression
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 15 (2022) no. 6, pp. 797-805

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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.
Keywords: weighted least-squares regression, Rank regression, shape parameter.
Mots-clés : Frechet distribution, Monte Carlo simulation 
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     author = {Zahia Khemissi and Brahim Brahimi and Fatah Benatia},
     title = {Heavy tail index estimator through weighted least-squares rank regression},
     journal = {\v{Z}urnal Sibirskogo federalʹnogo universiteta. Matematika i fizika},
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     year = {2022},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/JSFU_2022_15_6_a12/}
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Zahia Khemissi; Brahim Brahimi; Fatah Benatia. Heavy tail index estimator through weighted least-squares rank regression. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 15 (2022) no. 6, pp. 797-805. http://geodesic.mathdoc.fr/item/JSFU_2022_15_6_a12/