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of 4
pro vyhledávání: '"Fateh Benatia"'
Trimmed L-moments, were introduced by Elamir and Seheult(2003) to proposed a new estimation method for multi-parameter distributions when the mean doesn't exist or for heavy tailed distribution where the L-moments method which proposed by Hosking (19
Externí odkaz:
http://arxiv.org/abs/1607.06802
Publikováno v:
Journal of Siberian Federal University. Mathematics & Physics. :273-286
Inspired by L.Peng’s work on estimating the mean of heavy-tailed distribution in the case of completed data. we propose an alternative estimator and study its asymptotic normality when it comes to the right truncated random variable. A simulation s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20e5bb67cc38414a9b523480271e14b3
https://doi.org/10.17516/1997-1397-2021-14-3-273-286
https://doi.org/10.17516/1997-1397-2021-14-3-273-286
Autor:
Fateh Benatia, Djabrane Yahia
Publikováno v:
Afrika Statistika; Vol 7, No 1 (2012); 391-411
Afr. Stat. 7, no. 1 (2012), 391-411
Afr. Stat. 7, no. 1 (2012), 391-411
Let (Y;C;X) be a vector of random variables where Y; C and X are, respectively, the interest variable, a right censoring and a covariable (predictor). In this paper, we introduce a new nonlinear wavelet-based estimator of the regression function in t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4c3d6dba4de14998f42a550631476100
https://www.ajol.info/index.php/afst/article/view/83627
https://www.ajol.info/index.php/afst/article/view/83627
Publikováno v:
Afr. Stat. 6, no. 1 (2011), 335-345
Afrika Statistika; Vol 6, No 1 (2011); 335–345
Afrika Statistika; Vol 6, No 1 (2011); 335–345
A new semiparametric estimation method for multi-parameters Archimedean copulas based on the L-moments theory is proposed. Consistency and asymptotic normality of the defined estimator are established. Extensive simulation study to compare estimators
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::93de82d3f5929ab9407cb1f51f4e486f
https://doi.org/10.4314/afst.v6i1.3
https://doi.org/10.4314/afst.v6i1.3