Prediction for Progressively Type-II Censored Competing Risks Data from the Half-Logistic Distribution

DOI

10.2991/jsta.d.200224.004How to use a DOI?

Keywords

Maximum likelihood predictor; Bayesian prediction; Competing risks model; Progressive type-II censoring; Half-logistic distribution; Two-sample prediction; Simulation

Abstract

Point and interval predictions of the s-th order statistic in a future sample are discussed. The informative sample is assumed to be drawn from a general class of distributions which includes, among others, Weibull, compound Weibull, Pareto, Gompertz and half-logistic distributions. The informative and future samples are progressively type-II censored, under competing risks model, and assumed to be obtained from the same population. A special attention is paid to the half-logistic distribution. Using six different progressive censoring schemes, numerical computations are carried out to illustrate the performance of the procedure. An illustrative example based on real data is also considered. The biases, mean squared prediction errors of the maximum likelihood predictors, coverage probabilities and average interval lengths of the Bayesian prediction intervals are computed via a simulation study.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Essam K. AL-Hussaini
AU  - Alaa H. Abdel-Hamid
AU  - Atef F. Hashem
PY  - 2020
DA  - 2020/03/05
TI  - Prediction for Progressively Type-II Censored Competing Risks Data from the Half-Logistic Distribution
JO  - Journal of Statistical Theory and Applications
SP  - 36
EP  - 48
VL  - 19
IS  - 1
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.200224.004
DO  - 10.2991/jsta.d.200224.004
ID  - AL-Hussaini2020
ER  -