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Efficient maximum approximated likelihood inference for Tukey’s g-and-h distribution

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  • Xu, Ganggang
  • Genton, Marc G.

Abstract

Tukey’s g-and-h distribution has been a powerful tool for data exploration and modeling since its introduction. However, two long standing challenges associated with this distribution family have remained unsolved until this day: how to find an optimal estimation procedure and how to make valid statistical inference on unknown parameters. To overcome these two challenges, a computationally efficient estimation procedure based on maximizing an approximated likelihood function of Tukey’s g-and-h distribution is proposed and is shown to have the same estimation efficiency as the maximum likelihood estimator under mild conditions. The asymptotic distribution of the proposed estimator is derived and a series of approximated likelihood ratio test statistics are developed to conduct hypothesis tests involving two shape parameters of Tukey’s g-and-h distribution. Simulation examples and an analysis of air pollution data are used to demonstrate the effectiveness of the proposed estimation and testing procedures.

Suggested Citation

  • Xu, Ganggang & Genton, Marc G., 2015. "Efficient maximum approximated likelihood inference for Tukey’s g-and-h distribution," Computational Statistics & Data Analysis, Elsevier, vol. 91(C), pages 78-91.
    • Handle: RePEc:eee:csdana:v:91:y:2015:i:c:p:78-91
    DOI: 10.1016/j.csda.2015.06.002
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    References listed on IDEAS

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    1. Degen, Matthias & Embrechts, Paul & Lambrigger, Dominik D., 2007. "The Quantitative Modeling of Operational Risk: Between G-and-H and EVT," ASTIN Bulletin, Cambridge University Press, vol. 37(2), pages 265-291, November.
    2. M. C. Jones, 2015. "On Families of Distributions with Shape Parameters," International Statistical Review, International Statistical Institute, vol. 83(2), pages 175-192, August.
    3. He, Yulei & Raghunathan, Trivellore E., 2006. "Tukey's gh Distribution for Multiple Imputation," The American Statistician, American Statistical Association, vol. 60, pages 251-256, August.
    4. Yulei He & Trivellore E. Raghunathan, 2012. "Multiple imputation using multivariate gh transformations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(10), pages 2177-2198, June.
    5. Ganggang Xu & Suojin Wang & Jianhua Z. Huang, 2014. "Focused information criterion and model averaging based on weighted composite quantile regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 365-381, June.
    6. Xu, Yihuan & Iglewicz, Boris & Chervoneva, Inna, 2014. "Robust estimation of the parameters of g-and-h distributions, with applications to outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 66-80.
    7. Rayner, G. D. & MacGillivray, H. L., 2002. "Weighted quantile-based estimation for a class of transformation distributions," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 401-433, June.
    8. Kabir K. Dutta & David F. Babbel, 2002. "On Measuring Skewness and Kurtosis in Short Rate Distributions: The Case of the US Dollar London Inter Bank Offer Rates," Center for Financial Institutions Working Papers 02-25, Wharton School Center for Financial Institutions, University of Pennsylvania.
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