Fractional Linear-moments with Application - Mohamed Abd Elrazik, Enayat
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Recently, Elamir and Seheult (2003) introduced TL-moments as an extension of L-moments that depend on giving zero weight to extreme observations. TL-moments give more robust estimators than L-moments in the presence of outliers. Moreover, population TL-moments may be well defined where the corresponding population L-moments do not exist. Also, they discussed TL-moments when t (number of trimmed observations) is an integer number. The aim of this study is to extend the value of t from integer numbers to fractional numbers by introducing a new linear method of estimation which may be called…mehr

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Produktbeschreibung
Recently, Elamir and Seheult (2003) introduced TL-moments as an extension of L-moments that depend on giving zero weight to extreme observations. TL-moments give more robust estimators than L-moments in the presence of outliers. Moreover, population TL-moments may be well defined where the corresponding population L-moments do not exist. Also, they discussed TL-moments when t (number of trimmed observations) is an integer number. The aim of this study is to extend the value of t from integer numbers to fractional numbers by introducing a new linear method of estimation which may be called Fractional Linear -moments (FL-moments) as a generalization of the Trimmed L-moments method.
Autorenporträt
Enayat M. Abd Elrazik Assistant lecturer of statistics at Faculty of Commerce Benha University. She received her B. Sc. and M. Sc from Department of Statistics, Mathematics and Insurance Benha University.