Complete class of predictive densities for Type II censored data

Nobuyuki Ozeki and Takeshi Kurosawa

Communications in Statistics - Theory and Methods, 2025, vol. 54, issue 24, 7712-7730

Abstract: In this study, we discuss a complete class of predictive densities among posterior predictive densities using a Bayesian method and a plug-in predictive density based on the maximum likelihood estimate for Type II censored data without depending on the sample size of observations. This provides the best posterior predictive density with a lower risk under Kullback-Leibler loss. Furthermore, we specify the range of prior parameters whose posterior predictive densities dominate the plug-in predictive density for a fixed sample size. As a special case, when the shape parameter of the gamma prior is less than or equal to 2, the posterior predictive density consistently dominates the plug-in predictive density for any sample size.

Date: 2025
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DOI: 10.1080/03610926.2025.2481120

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