One- and Two-Sample Predictions Based on Progressively Type-II Censored Carbon Fibres Data Utilizing a Probability Model.
Comput Intell Neurosci
; 2022: 6416806, 2022.
Article
de En
| MEDLINE
| ID: mdl-35602617
ABSTRACT
New Weibull-Pareto distribution is a significant and practical continuous lifetime distribution, which plays an important role in reliability engineering and analysis of some physical properties of chemical compounds such as polymers and carbon fibres. In this paper, we construct the predictive interval of unobserved units in the same sample (one sample prediction) and the future sample based on the current sample (two-sample prediction). The used samples are generated from new Weibull-Pareto distribution due to a progressive type-II censoring scheme. Bayesian and maximum likelihood approaches are implemented to the prediction problems. In the Bayesian approach, it is not easy to simplify the predictive posterior density function in a closed form, so we use the generated Markov chain Monte Carlo samples from the Metropolis-Hastings technique with Gibbs sampling. Moreover, the predictive interval of future upper-order statistics is reported. Finally, to demonstrate the proposed methodology, both simulated data and real-life data of carbon fibres examples are considered to show the applicabilities of the proposed methods.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Théorème de Bayes
Type d'étude:
Health_economic_evaluation
/
Prognostic_studies
/
Risk_factors_studies
Langue:
En
Journal:
Comput Intell Neurosci
Sujet du journal:
INFORMATICA MEDICA
/
NEUROLOGIA
Année:
2022
Type de document:
Article
Pays d'affiliation:
Arabie saoudite