A simple approach to the estimation of Tukey's gh distribution

Luca Trapin, M. Bee, L. Trapin

Risultato della ricerca: Contributo in rivistaArticolo in rivista

8 Citazioni (Scopus)

Abstract

The Tukey's gh distribution is widely used in situations where skewness and elongation are important features of the data. As the distribution is defined through a quantile transformation of the normal, the likelihood function cannot be written in closed form and exact maximum likelihood estimation is unfeasible. In this paper we exploit a novel approach based on a frequentist reinterpretation of Approximate Bayesian Computation for approximating the maximum likelihood estimates of the gh distribution. This method is appealing because it only requires the ability to sample the distribution. We discuss the choice of the input parameters by means of simulation experiments and provide evidence of superior performance in terms of Root-Mean-Square-Error with respect to the standard quantile estimator. Finally, we give an application to operational risk measurement.
Lingua originaleEnglish
pagine (da-a)3287-3302
Numero di pagine16
RivistaJournal of Statistical Computation and Simulation
Volume86
DOI
Stato di pubblicazionePubblicato - 2016

Keywords

  • Applied Mathematics
  • Approximate maximum likelihood
  • Modeling and Simulation
  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • accept–reject algorithm
  • approximate Bayesian computation
  • risk measurement

Fingerprint Entra nei temi di ricerca di 'A simple approach to the estimation of Tukey's gh distribution'. Insieme formano una fingerprint unica.

Cita questo