Estimation and Prediction for Gompertz Distribution Under the Generalized Progressive Hybrid Censored Data

M. M. Mohie El-Din, M. Nagy and M. H. Abu-Moussa ()
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M. M. Mohie El-Din: Al-Azhar University
M. Nagy: King Saud University
M. H. Abu-Moussa: Cairo University

Annals of Data Science, 2019, vol. 6, issue 4, No 4, 673-705

Abstract: Abstract In this paper, the statistical inference for the Gompertz distribution based on generalized progressively hybrid censored data is discussed. The estimation of the parameters for Gompertz distribution is discussed using the maximum likelihood method and the Bayesian methods under different loss functions. The existence and uniqueness of the maximum likelihood estimation are proved. The point and interval Bayesian predictions for unobserved failures from the same sample and that from the future sample are derived. The Monte Carlo simulation is applied to compare the proposed methods. A real data example is used to apply the methods of estimation and to construct the prediction intervals.

Keywords: Bayesian estimation; Generalized progressive hyprid censored samples; Gompertz distribution; Maximum likelihood estimation; Bayesian prediction intervals; Primary 62G30; Secondary 62F15 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s40745-019-00199-3

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