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Estimating an endpoint with high order moments in the Weibull domain of attraction

  • Girard, Stéphane
  • Guillou, Armelle
  • Stupfler, Gilles

We present a method for estimating the endpoint of a unidimensional sample when the distribution function belongs to the Weibull-max domain of attraction. The approach relies on transforming the variable of interest and then using high order moments of the positive variable obtained this way. It is assumed that the order of the moments goes to infinity. We give conditions on the rate of divergence to get the weak and strong consistency as well as the asymptotic normality of the estimator. The good performance of the estimator is illustrated on some finite sample situations.

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Article provided by Elsevier in its journal Statistics & Probability Letters.

Volume (Year): 82 (2012)
Issue (Month): 12 ()
Pages: 2136-2144

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Handle: RePEc:eee:stapro:v:82:y:2012:i:12:p:2136-2144
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  1. Peter Hall & Julian Z. Wang, 2005. "Bayesian likelihood methods for estimating the end point of a distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 717-729.
  2. Deyuan Li & Liang Peng & Yongcheng Qi, 2011. "Empirical likelihood confidence intervals for the endpoint of a distribution function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 20(2), pages 353-366, August.
  3. Girard, Stéphane & Jacob, Pierre, 2008. "Frontier estimation via kernel regression on high power-transformed data," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 403-420, March.
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