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Likelihood inference for generalized Pareto distribution

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  • Castillo, Joan del
  • Serra, Isabel

Abstract

A new methodological approach that enables the use of the maximum likelihood method in the Generalized Pareto Distribution is presented. Thus several models for the same data can be compared under Akaike and Bayesian information criteria. The view is based on a detailed theoretical study of the Generalized Pareto Distribution submodels with compact support.

Suggested Citation

  • Castillo, Joan del & Serra, Isabel, 2015. "Likelihood inference for generalized Pareto distribution," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 116-128.
    • Handle: RePEc:eee:csdana:v:83:y:2015:i:c:p:116-128
    DOI: 10.1016/j.csda.2014.10.014
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    References listed on IDEAS

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    1. Joan Del Castillo & Jalila Daoudi & Richard Lockhart, 2014. "Methods to Distinguish Between Polynomial and Exponential Tails," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 382-393, June.
    2. 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, November.
    3. Aban, Inmaculada B. & Meerschaert, Mark M. & Panorska, Anna K., 2006. "Parameter Estimation for the Truncated Pareto Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 270-277, March.
    4. del Castillo, Joan & Daoudi, Jalila, 2009. "Estimation of the generalized Pareto distribution," Statistics & Probability Letters, Elsevier, vol. 79(5), pages 684-688, March.
    5. Song, Jongwoo & Song, Seongjoo, 2012. "A quantile estimation for massive data with generalized Pareto distribution," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 143-150, January.
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    Cited by:

    1. Marek Arendarczyk & Tomasz J. Kozubowski & Anna K. Panorska, 2022. "The Greenwood statistic, stochastic dominance, clustering and heavy tails," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 331-352, March.
    2. Hideki Nagatsuka & N. Balakrishnan, 2021. "Efficient likelihood-based inference for the generalized Pareto distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(6), pages 1153-1185, December.
    3. Xu Zhao & Zhongxian Zhang & Weihu Cheng & Pengyue Zhang, 2019. "A New Parameter Estimator for the Generalized Pareto Distribution under the Peaks over Threshold Framework," Mathematics, MDPI, vol. 7(5), pages 1-18, May.

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