Bayesian Prediction for Chris-Jerry Model Using Unified Progressive Hybrid Censored Sample
محورهای موضوعی : Operation ResearchHanieh Panahi 1 , Sahar Hosseinikhah Choshaly 2
1 - Department of Mathematics and Statistics, Lahijan Branch, Islamic Azad University, Lahijan, Iran
2 - Department of Management, Islamic Azad University, Lahijan Branch, Lahijan, Iran
کلید واژه: Bayesian point Prediction, HPD predictive, Chris-Jerry distribution, Unified Progressive Hybrid Censoring.,
چکیده مقاله :
From a real analysis standpoint, modeling hinges on the choice of the most suitable analysis method, whether it be a frequentist or Bayesian approach, to obtain an updated model. Modeling lifetime data with heavy tail has been a problem among many management researchers. Also, predicting unobserved data based on available data is one of the most important challenges in various sciences such as management and engineering. Bayesian predictive point in Chris-Jerry distribution is considered, in this paper. Te observed data is censored using a unified progressive hybrid censoring scheme. For evidence of the effectiveness of the given methodology, an application is explored.
From a real analysis standpoint, modeling hinges on the choice of the most suitable analysis method, whether it be a frequentist or Bayesian approach, to obtain an updated model. Modeling lifetime data with heavy tail has been a problem among many management researchers. Also, predicting unobserved data based on available data is one of the most important challenges in various sciences such as management and engineering. Bayesian predictive point in Chris-Jerry distribution is considered, in this paper. Te observed data is censored using a unified progressive hybrid censoring scheme. For evidence of the effectiveness of the given methodology, an application is explored.
[1] Gorny, J. and Cramer, E. (2018). Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring, Metrika. 81(2), 173-210.
[2] Nagy, M. and Alrasheedi, A.F. (2022). Classical and Bayesian Inference Using Type-II Unified Progressive Hybrid Censored Samples for Pareto Model, Appl Bionics Biomech. 2073067.
[3] Dutta, S. and Kayyal, S. (2024). Estimation and prediction for Burr type III distribution based on unified progressive hybrid censoring scheme, Journal of Applied Statistics, 1-33.
[4] Nik, A.S., Asgharzadeh, A. and Raqab, M. (2021). Estimation and prediction for a new Pareto-type distribution under progressive type-II censoring, Mathematics and Computers in Simulation, 190,508-530.
[5] Asadi, S. Panahi, H. Parviz, P. (2024). Estimation for Inverse Burr Distribution under Generalized Progressive Hybrid Censored data with an application to Wastewater Engineering Data. REVSTAT-Statistical Journal.
[6] Elshahhat, A., and Ashour, S. (2016). Bayesian and Non-Bayesian Estimation for Weibull Parameters Based on Generalized Type-II Progressive Hybrid Censoring Scheme. Pakistan Journal of Statistics and Operation Research, 12(2), 213-226.
[7] Onyekwere, CK. and Obulezi, OJ. (2022). Chris-Jerry Distribution and Its Applications, Asian Journal of Probability and Statistics, 20(1): 16-30.
[8] Chidera F. Innocent, Omoruyi A. Frederick, Edidiong M. Udofia, kechukwu J. Obulezi, Chinyere P. Igbokwe (2023). Estimation of the Parameters of the Power Size Biased Chris-Jerry Distribution, International Journal of Innovative Science and Research Technology, 5: 423-436.
[9] Okechukwu J. Obulezi, Ifean yi C. Anabike, Etaga Harrison O. (2023). Marshall-Olkin Chris-Jerry Distribution and its Applications, International Journal of Innovative Science and Research Technology, 8, 20-31.
[10] Chinedu, E.Q.; Chukwudum, Q.C.; Alsadat, N.; Obulezi, O.J.; Almetwally, E.M.; Tolba, A.H. New Lifetime Distribution with Applications to Single Acceptance Sampling Plan and Scenarios of Increasing Hazard Rates. Symmetry 2023, 15, 1881.
