Abstract:The progressive censoring scheme has received a considerable amount of attention in the last 15 years. During the last few years, joint progressive censoring scheme has gained some popularity. Recently, the authors Mondal and Kundu ("A New Two Sample Type-II Progressive Censoring Scheme," Communications in Statistics-Theory and Methods) introduced a balanced two-sample type II progressive censoring scheme and provided the exact inference when the two populations are exponentially distributed. In this article, … Show more
“…Also, Balakrishnan and Rasouli [8] presented exact likelihood inferences under jointly censoring schemes, Rasouli and Balakrishnan [9] discussed the exact likelihood inference under joint progressive Type-II censoring for two exponential populations, and Shafay et al [10] discussed the Bayes inference under joint Type-II censored sample for two exponential populations. And, this problem is handled recently by Al-Matrafi and Abd-Elmougod [11], Momenkhan and Abd-Elmougod [12], Mondal and Kundu [13], and Mondal andKundu [14]. e problem of statistical inference under jointly censoring schemes with the competing risks model is recently discussed by Almarashi et al [15].…”
The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.
“…Also, Balakrishnan and Rasouli [8] presented exact likelihood inferences under jointly censoring schemes, Rasouli and Balakrishnan [9] discussed the exact likelihood inference under joint progressive Type-II censoring for two exponential populations, and Shafay et al [10] discussed the Bayes inference under joint Type-II censored sample for two exponential populations. And, this problem is handled recently by Al-Matrafi and Abd-Elmougod [11], Momenkhan and Abd-Elmougod [12], Mondal and Kundu [13], and Mondal andKundu [14]. e problem of statistical inference under jointly censoring schemes with the competing risks model is recently discussed by Almarashi et al [15].…”
The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.
“…e Bayes estimators' explicit forms cannot be obtained, so the MCMC approach is impalement to get the (25,(0 (10) ,5, 0 (10) )) (1.5, 30,(0 (14) ,15, 0 (15) )) (25,(0 (10) ,5, 0 (10) )) (14) ,15, 0 (15) ))…”
Section: Discussionmentioning
confidence: 99%
“…Also, if J 1 � 1 as well as J 2 � 1, the parameters α i and β i are not estimable. Hence, the MLEs in (10), ( 11), (15), and (16) are only conditional MLEs, conditioned on 1<J 1 and 1<J 2 . Hence, the properties of the MLEs are discussed only as conditional on 1<J 1 and 1<J 2 [20].…”
Section: Maximum Likelihood Estimationmentioning
confidence: 99%
“…is study is extended for Weibull lifetime distribution by [14]. Recently, inferences of Weibull parameters are underbalance two-sample type-II progressive censoring scheme [15]. In a balanced joint progressive type-II censoring scheme, we suppose two lines of production, A 1 and A 2 , have the same kind of products under the same facility and the sample of size (κ 1 + κ 2 ) with size κ 1 from A 1 and κ 2 from A 2 are put under life testing.…”
The comparative life testing for products from different production lines under joint censoring schemes has received some attention over the past few years. Mondal and Kundu recently used the balanced joint progressive type-II censoring scheme to discuss the comparative exponential and Weibull populations. This paper implements the balanced censoring scheme with a hybrid progressive type-I censoring scheme known as a balanced joint progressive hybrid type-I censoring scheme (BJPHCS). The life Lomax products’ model formulation from two different lines of production with BJPHCS is discussed. The model parameters are estimated under maximum likelihood estimation for point and the corresponding asymptotic confidence intervals. Under independent gamma priors, the Bayes estimators and associated credible intervals are obtained with the help of MCMC technique. The validity of the theoretical results developed in this paper for estimation problems is discussed through numerical example and Monte Carlo simulation study, which report the estimators’ quality. Finally, we give a brief comment describing the numerical results.
“…On the other hand under certain set‐up, this joint censoring scheme, provides better estimation than conventional progressive Type‐II censoring schemes applied on two samples separately. For detail study see Mondal and Kundu 9‐11 . Due to these factors, application of the acceptance sampling plan under the BJPC scheme, can be regarded very beneficial and convenient in statistical quality control.…”
In statistical quality control, decision‐theoretic approach draws a significant amount of attention due to its economic considerations. In reliability life testing, decision‐theoretic approach has been used quite extensively under different censoring schemes. All these implementations are based on single sample of products coming from a particular source. In this work we study decision‐theoretic approach on two sample of products coming from two different sources under a joint censoring scheme when the life times are exponentially distributed. The major advantage of such implementation is to take decision on the acceptance or rejection of one or both the batches in a single life testing experiment. Decision making is performed based on minimizing the Bayes risk with respect to a given loss function. It is observed that under certain set‐ups, the joint censoring scheme is preferable over the two separate single sample censoring schemes in decision making.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.