scholarly journals Partially Accelerated Model for Analyzing Competing Risks Data from Gompertz Population under Type-I Generalized Hybrid Censoring Scheme

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Abdulaziz S. Alghamdi
Keyword(s):  
Accelerated Life Test ◽  
Type I ◽  
Test Model ◽  
Data Set ◽  
Hybrid Censoring ◽  
Lifetime Analysis ◽  
Competing Risks Data ◽  
Interval Estimators ◽  
Censoring Scheme ◽  
Causes Of Failure

In reliability engineering and lifetime analysis, many units of the product fail with different causes of failure, and some tests require stress higher than normal stress. Also, we need to design the life experiments which present methodology for formulating scientific and engineering problems using statistical models. So, in this paper, we adopted a partially constant stress accelerated life test model to present times to failure in a small period of time for Gompertz life products. Also, considering that, units are failing with the only two independent causes of failure and tested under type-I generalized hybrid censoring scheme the data built. Obtained data are analyzed with two methods of estimations, maximum likelihood and Bayes methods. These two methods are used to construct the point and interval estimators with the help of the MCMC method. The developed results are measured and compared under Monte Carlo studying. Also, a data set is analyzed for illustration purposes. Finally, some comments are presented to describe the numerical results.

scholarly journals Bound lengths for type-I progressive hybrid burr type-XII censored data under SS-PALT

2021 ◽  
Vol 10 (1) ◽  
pp. 4-22
Author(s):  
Gyan Prakash
Keyword(s):  
Confidence Intervals ◽  
Asymptotic Variance ◽  
Real Data ◽  
Accelerated Life Test ◽  
Type I ◽  
Unknown Parameters ◽  
Data Set ◽  
Ml Estimation ◽  
Hybrid Censoring ◽  
Burr Type Xii

Our main focus on combining two different approaches, Step-Stress Partially Accelerated Life Test and Type-I Progressive Hybrid censoring criteria in the present article. The fruitfulness of this combination has been investigated by bound lengths for unknown parameters of the Burr Type-XII distribution. Approximate confidence intervals, Bootstrap confidence intervals and One-Sample Bayes prediction bound lengths have been obtained under the above scenario. Particular cases of Type-I Progressive Hybrid censoring (Type-I and Progressive Type-II censoring) has also evaluated under SS-PALT. Optimal stress change time also measured by minimizing the asymptotic variance of ML Estimation. A simulation study based on Metropolis-Hastings algorithm have carried out along with a real data set example.

scholarly journals Accelerated competing risks model from Gompertz lifetime distributions with type-II censoring scheme

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 165-175
Author(s):  
Abdullah Almarashi ◽  
Gamal Abd-Elmougod
Keyword(s):  
Accelerated Life Test ◽  
Likelihood Method ◽  
Model Parameters ◽  
Test Model ◽  
Time To Failure ◽  
Type Ii ◽  
Type Ii Censoring ◽  
Accelerated Life ◽  
Interval Estimators ◽  
Censoring Scheme

Time to failure under normal stress conditions may take a long period of time and statistical inferences under this condition is more serious. Then, the experiment is loaded under stress higher than normal one which is defined as accelerated life tests. This problem in this paper is discussed in the form of partially step-stress accelerated life test model when the lifetime of the product has Gompertz lifetime distribution and unites are fails under the two independent risks. The maximum likelihood method under type-II censoring scheme is used to formulate the point and asymptotic confidence interval estimators of model parameters. The two boot?strap methods are also used to formulate the point and approximate interval estimators. The numerical results are adopted in the form of Monte Carlo studying to illustrate, assess and compare all of the theoretical results. Finally, results are discussed in points to clarify results validity.

scholarly journals Inference for Step-Stress Partially Accelerated Life Test Model with an Adaptive Type-I Progressively Hybrid Censored Data

2021 ◽  
Vol 19 (1) ◽  
pp. 2-20
Author(s):  
Showkat Ahmad Lone ◽  
Ahmadur Rahman ◽  
Tanveer A. Tarray
Keyword(s):  
Rayleigh Distribution ◽  
Accelerated Life Test ◽  
Model Parameters ◽  
Type I ◽  
Test Model ◽  
Hybrid Censoring ◽  
Accelerated Life Tests ◽  
Accelerated Life ◽  
Life Tests ◽  
Maximum Likelihood Estimations

Consider estimating data of failure times under step-stress partially accelerated life tests based on adaptive Type-I hybrid censoring. The mathematical model related to the lifetime of the test units is assumed to follow Rayleigh distribution. The point and interval maximum-likelihood estimations are obtained for distribution parameter and tampering coefficient. Also, the work is conducted under a traditional Type-I hybrid censoring plan (scheme). A Monte Carlo simulation algorithm is used to evaluate and compare the performances of the estimators of the tempering coefficient and model parameters under both progressively hybrid censoring plans. The comparison is carried out on the basis of mean squared errors and bias.

scholarly journals Estimating E-Bayesian of Parameters of Inverse Weibull Distribution Using an Unified Hybrid Censoring Scheme

2021 ◽  
pp. 113-122
Author(s):  
Shahram Yaghoobzadeh Shahrastani ◽  
Iman Makhdoom
Keyword(s):  
Monte Carlo Simulation ◽  
Real Data ◽  
Bayesian Estimator ◽  
Type I ◽  
Type Ii ◽  
Estimation Of Parameters ◽  
Data Set ◽  
Hybrid Censoring ◽  
Inverse Weibull Distribution ◽  
Censoring Scheme

The combination of generalization Type-I hybrid censoring and generalization Type-II hybrid censoring schemes, scheme creates a new censoring called a Unified hybrid censoring scheme. Therefore, in this study, the E-Bayesian estimation of parameters of the inverse Weibull (IW) distribution is obtained under the unified hybrid censoring scheme, and the efficiency of the proposed method was compared with the Bayesian estimator using Monte Carlo simulation and a real data set.

scholarly journals Statistical Analysis for Generalized Progressive Hybrid Censored Data from Lindley Distribution under Step-Stress Partially Accelerated Life Test Model

2021 ◽  
Vol 50 (1) ◽  
pp. 105-120
Author(s):  
Aakriti Pandey ◽  
Arun Kaushik ◽  
Sanjay K. Singh ◽  
Umesh Singh
Keyword(s):  
Mean Squared Error ◽  
Real Data ◽  
Estimation Procedure ◽  
Accelerated Life Test ◽  
Test Model ◽  
Life Test ◽  
Data Set ◽  
Lindley Distribution ◽  
Accelerated Life ◽  
Censoring Scheme

The aim of this paper is to present the estimation procedure for the step-stress partially accelerated life test model under the generalized progressive hybrid censoring scheme. The uncertainties are assumed to be governed by Lindley distribution. The problem with point and interval estimation of the parameters as well as the acceleration factor using maximum likelihood approach for the step-stress partially accelerated life test model has been considered. A simulation study is conducted to monitor the performance of the estimators on the basis of the mean squared error under the considered censoring scheme. The expected total time of the test under an accelerated condition is computed to examine the effects of the parameters on the duration of the test. In addition, a graph of the expected total time of the test under accelerated and un-accelerated conditions is provided to highlight the effect due to acceleration. One real data set has been analyzed for illustrative purposes.

scholarly journals Accelerated competing risks model from Gompertz lifetime distributions with type-II censoring scheme

2020 ◽  
Vol 24 (Suppl. 1) ◽  
pp. 165-175
Author(s):  
Abdullah Almarashi ◽  
Gamal Abd-Elmougod
Keyword(s):  
Accelerated Life Test ◽  
Likelihood Method ◽  
Model Parameters ◽  
Test Model ◽  
Time To Failure ◽  
Type Ii ◽  
Type Ii Censoring ◽  
Accelerated Life ◽  
Interval Estimators ◽  
Censoring Scheme

Time to failure under normal stress conditions may take a long period of time and statistical inferences under this condition is more serious. Then, the experiment is loaded under stress higher than normal one which is defined as accelerated life tests. This problem in this paper is discussed in the form of partially step-stress accelerated life test model when the lifetime of the product has Gompertz lifetime distribution and unites are fails under the two independent risks. The maximum likelihood method under type-II censoring scheme is used to formulate the point and asymptotic confidence interval estimators of model parameters. The two boot?strap methods are also used to formulate the point and approximate interval estimators. The numerical results are adopted in the form of Monte Carlo studying to illustrate, assess and compare all of the theoretical results. Finally, results are discussed in points to clarify results validity.

scholarly journals Bayesian and E-Bayesian Estimations of Bathtub-Shaped Distribution under Generalized Type-I Hybrid Censoring

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 934
Author(s):  
Yuxuan Zhang ◽  
Kaiwei Liu ◽  
Wenhao Gui
Keyword(s):  
Bayesian Method ◽  
Real Data ◽  
Loss Functions ◽  
Type I ◽  
Statistical Efficiency ◽  
Unknown Parameters ◽  
Data Set ◽  
Hybrid Censoring ◽  
Bayesian Estimations ◽  
Generalized Type

For the purpose of improving the statistical efficiency of estimators in life-testing experiments, generalized Type-I hybrid censoring has lately been implemented by guaranteeing that experiments only terminate after a certain number of failures appear. With the wide applications of bathtub-shaped distribution in engineering areas and the recently introduced generalized Type-I hybrid censoring scheme, considering that there is no work coalescing this certain type of censoring model with a bathtub-shaped distribution, we consider the parameter inference under generalized Type-I hybrid censoring. First, estimations of the unknown scale parameter and the reliability function are obtained under the Bayesian method based on LINEX and squared error loss functions with a conjugate gamma prior. The comparison of estimations under the E-Bayesian method for different prior distributions and loss functions is analyzed. Additionally, Bayesian and E-Bayesian estimations with two unknown parameters are introduced. Furthermore, to verify the robustness of the estimations above, the Monte Carlo method is introduced for the simulation study. Finally, the application of the discussed inference in practice is illustrated by analyzing a real data set.

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