Bayesian analysis of extreme values by mixture modeling

Leonardo Bottolo, Guido Consonni, Petros Dellaportas, Antonio Lijoi

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review


Modeling of extreme values in the presence of heterogeneity is still a relatively unexplored area. We consider losses pertaining to several related categories. For each category, we view exceedances over a given threshold as generated by a Poisson process whose intensity is regulated by a specific location, shape and scale parameter. Using a Bayesian approach, we develop a hierarchical mixture prior, with an unknown number of components, for each of the above parameters. Computations are performed using Reversible Jump MCMC. Our model accounts for possible grouping effects and takes advantage of the similarity across categories, both for estimation and prediction purposes. Some guidance on the specification of the prior distribution is provided, together with an assessment of inferential robustness. The method is illustrated throughout using a data set on large claims against a well-known insurance company over a 15-year period.
Lingua originaleEnglish
pagine (da-a)25-47
Numero di pagine23
Stato di pubblicazionePubblicato - 2003


  • Extreme value
  • Mixture model


Entra nei temi di ricerca di 'Bayesian analysis of extreme values by mixture modeling'. Insieme formano una fingerprint unica.

Cita questo