Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model

Abstract

In this article, based on the generalized Type-II progressive hybrid censoring sample from the Burr Type-XII distribution, maximum likelihood and Bayesian inference are discussed. Point and interval estimates of unknown parameters, reliability, and hazard functions are developed. We employed several loss functions, such as squared error, LINEX, and general entropy, as symmetric and asymmetric loss functions and various prior distributions as informative and non-informative priors for Bayesian inference of unknown parameters. Under a generalized Type-II progressive hybrid censoring sample, we also propose a Bayesian one-sample prediction for unobserved failures. We conduct simulation study using the MCMC algorithm for the Bayesian approach based on several prior distributions. Finally, we apply the results of the theoretical research to real data.

Computational Analysis for Fréchet Parameters of Life from Generalized Type-II Progressive Hybrid Censored Data with Applications in Physics and Engineering

“…Via the "coda" package (by Plummer et al [20]) in R 4.2.2 programming software, to obtain the Bayes point estimates along with their HPD interval estimates of the same unknown parameters, we simulated 12,000 MCMC samples and ignored the first 2000 iterations as burn-in. According to the prior mean and prior variance criteria, two sets called Prior-I and -II of the hyperparameters (a 1 , a 2 , b 1 , b 2 ) were considered as (2.5, 7.5, 5, 5) and (5,15,10,10), respectively. Specifically, the average point estimates (APEs) of δ, θ, R(t), or h(t) (say Ω) were given by…”

“…Seo [13] developed an objective Bayesian analysis with limited information about the Weibull distribution. The competing risks from exponential data were addressed by Cho and Lee [14], and more recently, Nagy et al [15] looked at both the point and interval estimates of the Burr-XII parameters, and Wang et al [16] addressed the estimation problem of the Kumaraswamy parameters using classical and Bayesian procedures.…”

Fuzzy multicomponent stress-strength reliability in presence of partially accelerated life testing under generalized progressive hybrid censoring scheme subject to inverse Weibull model

“…Yousef et al [16] used the generalized progressive hybrid censoring design to discuss the inference of stress-strength model based on the exponentiated exponential distribution. Nagy et al [17] discussed the generalized Type-II progressive hybrid censoring sample from the Burr Type-XII distribution. Wang et al [18] discussed the inference of Kumaraswamy distribution under generalized progressive hybrid censoring.…”

Estimations of Generalized Exponential Distribution Parameters Based on Type I Generalized Progressive Hybrid Censored Data

“…Furthermore, because there are more failed observations, statistical estimating efficiency improves. The Type I generalized PHCS have been investigated for instance by El-Din et al [ 9 ], Nagy et al [ 10 , 11 ], and Nagy and Alrasheedi [ 12 ].…”