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

• Arthur Pewsey ^[1]
  1. [1] Universidad de Extremadura

     Universidad de Extremadura

     Badajoz, España

     
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 27, Nº. 1, 2018 (Ejemplar dedicado a: Special issue on goodness of fit (GOF)), págs. 147-172
• Idioma: inglés
• DOI: 10.1007/s11749-017-0538-2
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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