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

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 funct...

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Bibliographic Details
Published in:Annals of data science 2019-12, Vol.6 (4), p.673-705
Main Authors: Mohie El-Din, M. M., Nagy, M., Abu-Moussa, M. H.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Bayesian analysis
Business and Management
Censored data (mathematics)
Computer simulation
Economic models
Economics
Finance
Insurance
Management
Maximum likelihood estimation
Maximum likelihood method
Monte Carlo simulation
Parameter estimation
Statistical inference
Statistics for Business
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Summary: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.
ISSN:2198-5804
2198-5812
DOI:10.1007/s40745-019-00199-3