Advances in statistical decision theory and applications
On estimation based on progressive first-failure-censored sampling
Computational Statistics & Data Analysis
Prediction for Pareto distribution based on progressively Type-II censored samples
Computational Statistics & Data Analysis
Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction
Information Sciences: an International Journal
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In this article, we consider the problem of predicting times to failure of units censored in multiple stages in a progressively censored sample from an absolutely continuous population. The best linear unbiased predictors (BLUP), the maximum-likelihood predictors (MLP), and the conditional median predictors (CMP) are considered. The properties of MLP such as unbiasedness, consistency and efficiency are examined. The MLP or modified MLP (MMLP) are derived for exponential and extreme value populations. In addition, for these populations, the conditional distributions are used to derive the CMP. Comparison of different predictors are made with respect to mean squared prediction error (MSPE). Finally, some numerical examples are presented to illustrate all the prediction methods discussed here. Using simulation studies, prediction intervals are also generated for these examples.