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Parametric bootstrap edf-based goodness-of-fit testing for sinh–arcsinh distributions

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  • Arthur Pewsey

    (University of Extremadura)

Abstract

Four-parameter sinh–arcsinh classes provide flexible distributions with which to model skew, as well as light- or heavy-tailed, departures from a symmetric base distribution. A quantile-based method of estimating their parameters is proposed and the resulting estimates advocated as starting values from which to initiate maximum likelihood estimation. Parametric bootstrap edf-based goodness-of-fit tests for sinh–arcsinh distributions are proposed, and their operating characteristics for small- to medium-sized samples explored in Monte Carlo experiments. The developed methodology is illustrated in the analysis of data on the body mass index of athletes and the depth of snow on an Antarctic ice floe.

Suggested Citation

  • Arthur Pewsey, 2018. "Parametric bootstrap edf-based goodness-of-fit testing for sinh–arcsinh distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 147-172, March.
    • Handle: RePEc:spr:testjl:v:27:y:2018:i:1:d:10.1007_s11749-017-0538-2
    DOI: 10.1007/s11749-017-0538-2
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    References listed on IDEAS

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    1. Jones, M. C. & Rosco, J. F. & Pewsey, Arthur, 2011. "Skewness-Invariant Measures of Kurtosis," The American Statistician, American Statistical Association, vol. 65(2), pages 89-95.
    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. Arthur Pewsey & Toshihiro Abe, 2015. "The sinh-arcsinhed logistic family of distributions: properties and inference," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 573-594, June.
    4. Pingel, Ronnie, 2014. "Some approximations of the logistic distribution with application to the covariance matrix of logistic regression," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 63-68.
    5. Matsumoto, Ken'ichi & Voudouris, Vlasios & Stasinopoulos, Dimitrios & Rigby, Robert & Di Maio, Carlo, 2012. "Exploring crude oil production and export capacity of the OPEC Middle East countries," Energy Policy, Elsevier, vol. 48(C), pages 820-828.
    6. Arthur Pewsey, 2000. "Problems of inference for Azzalini's skewnormal distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(7), pages 859-870.
    7. McKeague, Ian W., 2015. "Central limit theorems under special relativity," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 149-155.
    8. Voudouris, Vlasios & Stasinopoulos, Dimitrios & Rigby, Robert & Di Maio, Carlo, 2011. "The ACEGES laboratory for energy policy: Exploring the production of crude oil," Energy Policy, Elsevier, vol. 39(9), pages 5480-5489, September.
    9. Winfried Stute & Wenceslao Manteiga & Manuel Quindimil, 1993. "Bootstrap based goodness-of-fit-tests," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 40(1), pages 243-256, December.
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