An optimal L-statistics quantile estimator for a set of location–scale populations
AbstractThis paper presents an L-statistics quantile estimator for estimating the pth quantile of a population which belongs to a set of location–scale distributions. The design of the weight vector of the estimator is formulated as a constrained optimization problem. The objective of the optimization problem is to minimize the mean square error. The optimization problem is subject to a unitary constraint on the weight vector of the L-statistics quantile estimation. We solve the optimization problem and obtain an optimal solution, which is the weight vector of the proposed estimator.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 82 (2012)
Issue (Month): 10 ()
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Brodin, Erik, 2006. "On quantile estimation by bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1398-1406, March.
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