Exact Likelihood Inference for a Competing Risks Model with Generalized Type II Progressive Hybrid Censored Exponential Data

Abstract

In many situations of survival and reliability test, the withdrawal of units from the test is pre-planned in order to to free up testing facilities for other tests, or to save cost and time. It is known that several risk factors (RiFs) compete for the immediate failure cause of items. In this paper, we derive an inference for a competing risks model (CompRiM) with a generalized type II progressive hybrid censoring scheme (GeTy2PrHCS). We derive the conditional moment generating functions (CondMgfs), distributions and confidence interval (ConfI) of the scale parameters of exponential distribution (ExDist) under GeTy2PrHCS with CompRiM. A real data set is analysed to illustrate the validity of the method developed here. From the data, it can be seen that the conditional PDFs of MLEs is almost symmetrical.

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

“…Applying the delta approach to (14), the approximation of the variance for the Fr distribution's log( T ) is obtained as…”

“…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.…”

Statistical Analysis of Type-II Generalized Progressively Hybrid Alpha-PIE Censored Data and Applications in Electronic Tubes and Vinyl Chloride

“…In the context of GTIIPHC data, several researchers have carried out important research on the statistical estimation of unknown parameter(s) and/or reliability time functions in various lifetime models; for example, Ashour and Elshahhat [11] studied both frequentist and Bayes estimators of the Weibull parameters; Ateya and Mohammed [12] studied the prediction issue of the Burr-XII failure times; Seo [13] discussed the Bayesian inference of Weibull's model; Cho and Lee [14] analyzed the competing risks from exponential data; Nagy et al [15] pointed out different estimates of the Burr-XII parameters; Wang et al [16] derived various estimators of the Kumaraswamy parameters; Elshahhat et al [17] addressed the Nadarajah-Haghighi parameters; later, Alotaibi et al [18] estimated the Fréchet Parameters. Although there are many studies that give a mathematical treatment to the proposed distribution, they do not shed light on the application aspects of the alpha-PIE distribution, especially in reliable practice.…”

Inference for Generalized Progressive Hybrid Type-II Censored Weibull Lifetimes under Competing Risk Data.

“…Balakrishnan and Aggarwala [1] and Wu [16] proposed another type of censoring called progressive censoring allows removal of units from the test at times other than the final termination point. Cho and Lee [5], derived point and interval estimations for the unknown parameters of exponential distribution under generalized progressive hybrid type-II (GPHT-II) censoring in presence of competing risks data when the cause of failure is known.…”