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An optimal L-statistics quantile estimator for a set of location–scale populations

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  • Li, Ling-Wei
  • Lee, Loo-Hay
  • Chen, Chun-Hung
  • Guo, Bo
  • Liu, Ya-Jie

Abstract

This 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.

Suggested Citation

  • Li, Ling-Wei & Lee, Loo-Hay & Chen, Chun-Hung & Guo, Bo & Liu, Ya-Jie, 2012. "An optimal L-statistics quantile estimator for a set of location–scale populations," Statistics & Probability Letters, Elsevier, vol. 82(10), pages 1853-1858.
  • Handle: RePEc:eee:stapro:v:82:y:2012:i:10:p:1853-1858
    DOI: 10.1016/j.spl.2012.05.015
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    References listed on IDEAS

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    1. Brodin, Erik, 2006. "On quantile estimation by bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1398-1406, March.
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