Bayesian Estimation for Inverse Weibull Distribution Under Progressive Type-II Censored Data With Beta-Binomial Removals

Abstract

This paper deals with the estimation procedure for inverse Weibull distribution under progressive type-II censored samples when removals follow Beta-binomial probability law. To estimate the unknown parameters, the maximum likelihood and Bayes estimators are obtained under progressive censoring scheme mentioned above. Bayes estimates are obtained using Markov chain Monte Carlo (MCMC) technique considering square error loss function and compared with the corresponding MLE's. Further, the expected total time on test is obtained under considered censoring scheme. Finally, a real data set has been analysed to check the validity of the study.

On Entropy Estimation of Inverse Weibull Distribution under Improved Adaptive Progressively Type-II Censoring with Applications

“…1) consists of the vinyl chloride data (in mg/L) obtained from clean-up-gradient monitoring wells. Several researchers analyzed the data; see Bhaumik et al [42], Vishwakarma et al [43], and Okasha and Nassar [13]. The second dataset (referred to as Data No.…”

Bayesian Life Analysis of Generalized Chen's Population Under Progressive Censoring

“…This data set, reported by Efron (1988), represents the survival times (in days) of a group of 44 patients suffering from Head and Neck cancer (HNC) disease and treated using a combination of radiotherapy and chemotherapy (RT+CT). The ordered data times are: 12.2, 23.56, 23.74, 25.87, 31.98, 37, 41.35, 47.38, 55.46, 58.36, 63.47, 68.46, 74.47, 78.26, 81.43, 84, 92, 94, 110, 112, 119, 127, 130, 133, 140, 146, 155, 159, 173, 179, 194, 195, 209, 249, 281, 319, 339, 432, 469, 519, 633, 725, 817, 1776. Recently, this data set was also analyzed by several authors, e.g., see Sharma et al (2015), Sharma (2018) and Vishwakarma et al (2018). First, we fit the GCD to the complete data set along ten popular lifetime distributions as its competitors, namely: CD, Gompertz distribution (GD), Hjorth distribution (HD), Weibull distribution (WD), generalized Pareto distribution (GPD), exponentiated Pareto distribution (EPD), generalized-exponential distribution (GED), generalized Rayleigh distribution (GRD), generalized half-logistic distribution (GHLD) and Nadarajah-Haghighi distribution (NHD) with their respective PDFs (for x > 0 and α, β, λ > 0), see Table 11.…”