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

Arthur Pewsey ()
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Arthur Pewsey: University of Extremadura

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2018, vol. 27, issue 1, No 8, 147-172

Abstract: 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.

Keywords: Anderson–Darling statistic; Logistic distribution; Normal distribution; Quantile-based estimation; Sinh–arcsinh transformation; t-distribution; 62F40; 62F03; 62F10 (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11749-017-0538-2

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