Bayesian prediction regions and density estimation with type-2 censored data

Akbar Asgharzadeh; Éric Marchand; Ali Saadati Nik

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arXiv · 2403.06718

Bayesian prediction regions and density estimation with type-2 censored data

Akbar Asgharzadeh·Éric Marchand·Ali Saadati Nik

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Abstract

For exponentially distributed lifetimes, we consider the prediction of future order statistics based on having observed the first $m$ order statistics. We focus on the previously less explored aspects of predicting: (i) an arbitrary pair of future order statistics such as the next and last ones, as well as (ii) the next $N$ future order statistics. We provide explicit and exact Bayesian credible regions associated with Gamma priors, and constructed by identifying a region with a given credibility $1-\lambda$ under the Bayesian predictive density. For (ii), the HPD region is obtained, while a two-step algorithm is given for (i). The predictive distributions are represented as mixtures of bivariate Pareto distributions, as well as multivariate Pareto distributions. For the non-informative prior density choice, we demonstrate that a resulting Bayesian credible region has matching frequentist coverage probability, and that the resulting predictive density possesses the optimality properties of best invariance and minimaxity.

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Akbar Asgharzadeh, Éric Marchand, Ali Saadati Nik. 2024-03-11. Bayesian prediction regions and density estimation with type-2 censored data. https://arxiv.org/abs/2403.06718

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