The two most commonly used methods for Bayesian set estimation of an unknown one-dimensional parameter are equal-tails and highest posterior density intervals. The resulting estimates may be numerically different for specific observed samples but they tend to become closer and closer as the sample size increases. In this article we consider a pre-posterior measure of the progressive overlap between these two types of intervals and relationships with the skewness of the posterior distribution. We illustrate the implementation of the method for the Rayleigh model that is often used in the context of reliability and survival analysis.
Predictive discrepancy of credible intervals for the parameter of the Rayleigh distribution / DE SANTIS, Fulvio; Gubbiotti, Stefania. - (2020), pp. 697-701. (Intervento presentato al convegno SIS 2020 tenutosi a PISA).
Predictive discrepancy of credible intervals for the parameter of the Rayleigh distribution
Fulvio De Santis;Stefania Gubbiotti
2020
Abstract
The two most commonly used methods for Bayesian set estimation of an unknown one-dimensional parameter are equal-tails and highest posterior density intervals. The resulting estimates may be numerically different for specific observed samples but they tend to become closer and closer as the sample size increases. In this article we consider a pre-posterior measure of the progressive overlap between these two types of intervals and relationships with the skewness of the posterior distribution. We illustrate the implementation of the method for the Rayleigh model that is often used in the context of reliability and survival analysis.File | Dimensione | Formato | |
---|---|---|---|
DeSanctis_Predictive-discrepancy_2020.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
366.22 kB
Formato
Adobe PDF
|
366.22 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.