Pub Date : 2022-10-31DOI: 10.1080/16843703.2022.2128241
Yi-Feng Niu, Yichang Song, Xiu-Zhen Xu
ABSTRACT This paper models a logistics network as a multi-distribution multi-state flow network (MFN) in which each arc has a random capacity characterized by more than one probability distribution under different budget allocations. An optimization model is constructed to minimize the total budget required for the network to achieve a given reliability level. By integrating a novel budget vector method with the well-known binary search method, an effective and efficient algorithm is developed to solve the optimization model, together with analyses on the computational complexity. A practical implementation on a simple network is presented to illustrate the proposed method, and the computational efficiency is explored via numerical examples. Finally, a real case study is provided to showcase the application of the proposed model and method.
{"title":"Budget optimization for a multi-distribution multi-state logistics network with reliability consideration","authors":"Yi-Feng Niu, Yichang Song, Xiu-Zhen Xu","doi":"10.1080/16843703.2022.2128241","DOIUrl":"https://doi.org/10.1080/16843703.2022.2128241","url":null,"abstract":"ABSTRACT This paper models a logistics network as a multi-distribution multi-state flow network (MFN) in which each arc has a random capacity characterized by more than one probability distribution under different budget allocations. An optimization model is constructed to minimize the total budget required for the network to achieve a given reliability level. By integrating a novel budget vector method with the well-known binary search method, an effective and efficient algorithm is developed to solve the optimization model, together with analyses on the computational complexity. A practical implementation on a simple network is presented to illustrate the proposed method, and the computational efficiency is explored via numerical examples. Finally, a real case study is provided to showcase the application of the proposed model and method.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"528 - 544"},"PeriodicalIF":2.8,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43657268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-24DOI: 10.1080/16843703.2022.2136281
Yiying Zhang
ABSTRACT It is not uncommon that the living situation of a unit (e.g. a component in a reliability system) plays a critical role in affecting the performance of another unit or even the whole system. The study on stochastic behaviors of conditional residual lifetimes appears to be particularly important for analyzing the performances of interested units. This paper studies stochastic comparisons on the conditional residual lifetimes of units under the dependent information when another unit has survived up to a given time. Sufficient (and necessary) conditions are established for comparing conditional residual lifetimes of different units under various dependence structures as well as different events according to some traditional stochastic orders. We provide numerical examples based on the well-known FGM copula and Archimedean copulas to verify the conditions. Three real scenarios in reliability theory and risk theory are also considered to show the applicability of our results.
{"title":"Stochastic comparisons of conditional residual lifetimes with applications","authors":"Yiying Zhang","doi":"10.1080/16843703.2022.2136281","DOIUrl":"https://doi.org/10.1080/16843703.2022.2136281","url":null,"abstract":"ABSTRACT It is not uncommon that the living situation of a unit (e.g. a component in a reliability system) plays a critical role in affecting the performance of another unit or even the whole system. The study on stochastic behaviors of conditional residual lifetimes appears to be particularly important for analyzing the performances of interested units. This paper studies stochastic comparisons on the conditional residual lifetimes of units under the dependent information when another unit has survived up to a given time. Sufficient (and necessary) conditions are established for comparing conditional residual lifetimes of different units under various dependence structures as well as different events according to some traditional stochastic orders. We provide numerical examples based on the well-known FGM copula and Archimedean copulas to verify the conditions. Three real scenarios in reliability theory and risk theory are also considered to show the applicability of our results.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"601 - 632"},"PeriodicalIF":2.8,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47036777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-16DOI: 10.1080/16843703.2022.2124778
Pingye Gong, Qiming Xia, Jie Xuan, A. Saghir, Baocai Guo
ABSTRACT Studies on control charts with estimated parameters have received much attention in the recent literature. In this paper, the effect of parameter estimation on the performance of the Shewhart-type chart for the Rayleigh distribution, namely the chart, is first studied under the conditional perspective. It is found that parameter estimation has a serious effect on the performance of the frequentist chart. In order to solve this problem, the frequentist chart is adjusted by using the exceedance probability criterion to guarantee the in-control performance. Since the frequentist chart uses the sample information from Phase I, but not the process information from past experience, an alternative chart, namely the Bayesian chart, is proposed based on the predictive distribution of the plotting statistic. The performances of the Bayesian and adjusted frequentist charts are evaluated and compared in terms of the percentiles, mean, and standard deviation of the conditional average run length distribution. The results suggest that the Bayesian chart outperforms the frequentist counterpart, especially when more prior information is available.
{"title":"Design of Shewhart-type control charts with estimated parameter for the Rayleigh distribution using frequentist and Bayesian approaches","authors":"Pingye Gong, Qiming Xia, Jie Xuan, A. Saghir, Baocai Guo","doi":"10.1080/16843703.2022.2124778","DOIUrl":"https://doi.org/10.1080/16843703.2022.2124778","url":null,"abstract":"ABSTRACT Studies on control charts with estimated parameters have received much attention in the recent literature. In this paper, the effect of parameter estimation on the performance of the Shewhart-type chart for the Rayleigh distribution, namely the chart, is first studied under the conditional perspective. It is found that parameter estimation has a serious effect on the performance of the frequentist chart. In order to solve this problem, the frequentist chart is adjusted by using the exceedance probability criterion to guarantee the in-control performance. Since the frequentist chart uses the sample information from Phase I, but not the process information from past experience, an alternative chart, namely the Bayesian chart, is proposed based on the predictive distribution of the plotting statistic. The performances of the Bayesian and adjusted frequentist charts are evaluated and compared in terms of the percentiles, mean, and standard deviation of the conditional average run length distribution. The results suggest that the Bayesian chart outperforms the frequentist counterpart, especially when more prior information is available.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"450 - 467"},"PeriodicalIF":2.8,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42473284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-10DOI: 10.1080/16843703.2022.2124789
Wei Zhao, Chunjie Wu
ABSTRACT Many applications, especially in the industrial or medical fields, frequently encounter window-censored alternating renewal process (ARP). Process monitoring for window-censored ARP observations has received increasing attention recently. However, some conventional methods are inadequate as they are mainly designed considering only one censoring mechanism. In this paper, we utilize a ‘residual life’ distribution, and propose a novel online monitoring strategy that combines the likelihood with all kinds of censoring information, along with a modified conditional expected value exponentially weighted moving average control chart. The construction and implementation of these control schemes are studied by simulations which give desirable in-control and out-of-control performances and are robust under various scenarios. Finally, this paper shows by example based on driver’s glance data that the proposed methods have a good monitoring effect in practical scenarios.
{"title":"Monitoring the alternating renewal processes with Weibull window-censored data","authors":"Wei Zhao, Chunjie Wu","doi":"10.1080/16843703.2022.2124789","DOIUrl":"https://doi.org/10.1080/16843703.2022.2124789","url":null,"abstract":"ABSTRACT Many applications, especially in the industrial or medical fields, frequently encounter window-censored alternating renewal process (ARP). Process monitoring for window-censored ARP observations has received increasing attention recently. However, some conventional methods are inadequate as they are mainly designed considering only one censoring mechanism. In this paper, we utilize a ‘residual life’ distribution, and propose a novel online monitoring strategy that combines the likelihood with all kinds of censoring information, along with a modified conditional expected value exponentially weighted moving average control chart. The construction and implementation of these control schemes are studied by simulations which give desirable in-control and out-of-control performances and are robust under various scenarios. Finally, this paper shows by example based on driver’s glance data that the proposed methods have a good monitoring effect in practical scenarios.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"468 - 484"},"PeriodicalIF":2.8,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47101223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-03DOI: 10.1080/16843703.2022.2124644
H-Y. Lyu, Hongcheng Qu, Shuai Wang, Li Ma, Zaiyou Yang
ABSTRACT In this paper, we introduce the concept of a series system with two components and three shock sources considering degradation to build a reliability model. Sources 1 and 2 affect components 1 and 2, respectively. Source 3 covers both components. Both components are subject to dependent competing failure processes (DCFPs). A general reliability model of the n-component series system with m-shock sources subject to DCFPs is derived. The phase-type distribution method is applied to calculate the reliability of the hard failure process. The time lag among shocks follows the continuous phase-type distribution (PH c ). The lifetime and system reliability properties are discussed based on the phase-type distribution. The dependence of shock sources is also considered according to the proposition of phase-type distribution (PH). Finally, an application example and sensitivity analysis of micro-electro-mechanical systems (MEMS) oscillators subject to various shock models are presented to illustrate the developed reliability models.
{"title":"Reliability model of series systems with multiple shock sources subject to dependent competing failure processes using phase-type distribution","authors":"H-Y. Lyu, Hongcheng Qu, Shuai Wang, Li Ma, Zaiyou Yang","doi":"10.1080/16843703.2022.2124644","DOIUrl":"https://doi.org/10.1080/16843703.2022.2124644","url":null,"abstract":"ABSTRACT In this paper, we introduce the concept of a series system with two components and three shock sources considering degradation to build a reliability model. Sources 1 and 2 affect components 1 and 2, respectively. Source 3 covers both components. Both components are subject to dependent competing failure processes (DCFPs). A general reliability model of the n-component series system with m-shock sources subject to DCFPs is derived. The phase-type distribution method is applied to calculate the reliability of the hard failure process. The time lag among shocks follows the continuous phase-type distribution (PH c ). The lifetime and system reliability properties are discussed based on the phase-type distribution. The dependence of shock sources is also considered according to the proposition of phase-type distribution (PH). Finally, an application example and sensitivity analysis of micro-electro-mechanical systems (MEMS) oscillators subject to various shock models are presented to illustrate the developed reliability models.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"419 - 449"},"PeriodicalIF":2.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41318297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-03DOI: 10.1080/16843703.2022.2116903
Zhiqiong Wang, Zhen He, Yanfen Shang, Yanhui Ma
ABSTRACT Statistical process control for count data has attracted increasing attention in recent years. The need for efficient control charts suitable for autocorrelated multivariate count processes is well recognized. However, there is a scarcity of research aiming to take into account the autocorrelation among the multivariate count data. We are motivated to study the Phase I analysis of autocorrelated multivariate Poisson processes to detect and estimate change points in reference datasets. A change-point method is proposed based on the multivariate Poisson INAR(1) model by integrating generalized likelihood ratio tests with the binary segmentation procedure. A diagnostic procedure for pinpointing the location of the change point is also discussed. Our simulation results show that the proposed method has a better performance than the benchmark method, across a range of possible shifts, in the detection effectiveness and diagnostic accuracy. Furthermore, a real example from the manufacturing industry is used to illustrate the implementation steps of the proposed method.
{"title":"Change-Point detection for autocorrelated multivariate Poisson processes","authors":"Zhiqiong Wang, Zhen He, Yanfen Shang, Yanhui Ma","doi":"10.1080/16843703.2022.2116903","DOIUrl":"https://doi.org/10.1080/16843703.2022.2116903","url":null,"abstract":"ABSTRACT Statistical process control for count data has attracted increasing attention in recent years. The need for efficient control charts suitable for autocorrelated multivariate count processes is well recognized. However, there is a scarcity of research aiming to take into account the autocorrelation among the multivariate count data. We are motivated to study the Phase I analysis of autocorrelated multivariate Poisson processes to detect and estimate change points in reference datasets. A change-point method is proposed based on the multivariate Poisson INAR(1) model by integrating generalized likelihood ratio tests with the binary segmentation procedure. A diagnostic procedure for pinpointing the location of the change point is also discussed. Our simulation results show that the proposed method has a better performance than the benchmark method, across a range of possible shifts, in the detection effectiveness and diagnostic accuracy. Furthermore, a real example from the manufacturing industry is used to illustrate the implementation steps of the proposed method.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"384 - 404"},"PeriodicalIF":2.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47887235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-03DOI: 10.1080/16843703.2022.2125762
R. Kumari, Y. Tripathi, R. Sinha, Liang Wang
ABSTRACT We consider estimation of reliability in a multicomponent system when data are observed under Type-II censoring. Various estimates of this parametric function are derived when stress and strength (SS) variables follow inverse exponentiated distributions with a common scale parameter. We first obtain maximum likelihood estimate of the reliability. Then, approximate confidence intervals are obtained based on asymptotic theory. Further useful pivotal quantities are constructed and in sequel alternative estimates of the reliability are derived. The case where all parameters of SS components are unknown is also studied and various estimates for the reliability are proposed. Equivalence testing between model parameters is discussed as well. Performance of all estimates is compared using simulations and comments are derived. We analyze two real data sets for illustration purposes.
{"title":"Reliability estimation for the inverted exponentiated Pareto distribution","authors":"R. Kumari, Y. Tripathi, R. Sinha, Liang Wang","doi":"10.1080/16843703.2022.2125762","DOIUrl":"https://doi.org/10.1080/16843703.2022.2125762","url":null,"abstract":"ABSTRACT We consider estimation of reliability in a multicomponent system when data are observed under Type-II censoring. Various estimates of this parametric function are derived when stress and strength (SS) variables follow inverse exponentiated distributions with a common scale parameter. We first obtain maximum likelihood estimate of the reliability. Then, approximate confidence intervals are obtained based on asymptotic theory. Further useful pivotal quantities are constructed and in sequel alternative estimates of the reliability are derived. The case where all parameters of SS components are unknown is also studied and various estimates for the reliability are proposed. Equivalence testing between model parameters is discussed as well. Performance of all estimates is compared using simulations and comments are derived. We analyze two real data sets for illustration purposes.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"485 - 510"},"PeriodicalIF":2.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48400620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-27DOI: 10.1080/16843703.2022.2126260
Baocai Guo, Qiming Xia, Yingying Sun, Muhammad Shahzad Aslam
ABSTRACT Process capability index (PCI) is widely used to evaluate the process capability in various industries. In this article, generalized confidence intervals (GCIs) of two widely used percentile-based PCIs for the inverse Gaussian (IG) distribution are constructed. Meanwhile, the GCI of the difference between two IG processes’ PCIs is also considered. In addition, the performance of the proposed GCI is evaluated and compared with that of the confidence intervals based on the traditional bootstrap method. The simulation results indicate that the GCI outperforms the bootstrap counterparts in terms of the coverage probabilities, and the actual coverage probabilities of the GCI are sufficiently close to the target value. Finally, two real examples are presented to illustrate the implementation of the proposed method.
{"title":"Generalized confidence intervals of quantile-based process capability indices for inverse Gaussian distribution","authors":"Baocai Guo, Qiming Xia, Yingying Sun, Muhammad Shahzad Aslam","doi":"10.1080/16843703.2022.2126260","DOIUrl":"https://doi.org/10.1080/16843703.2022.2126260","url":null,"abstract":"ABSTRACT Process capability index (PCI) is widely used to evaluate the process capability in various industries. In this article, generalized confidence intervals (GCIs) of two widely used percentile-based PCIs for the inverse Gaussian (IG) distribution are constructed. Meanwhile, the GCI of the difference between two IG processes’ PCIs is also considered. In addition, the performance of the proposed GCI is evaluated and compared with that of the confidence intervals based on the traditional bootstrap method. The simulation results indicate that the GCI outperforms the bootstrap counterparts in terms of the coverage probabilities, and the actual coverage probabilities of the GCI are sufficiently close to the target value. Finally, two real examples are presented to illustrate the implementation of the proposed method.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"405 - 417"},"PeriodicalIF":2.8,"publicationDate":"2022-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41779874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-06DOI: 10.1080/16843703.2022.2116265
Junxiang Li, Jianqiao Chen, Jun-hong Wei, Xinhua Yang
ABSTRACT Reliability analysis methods such as active learning Kriging surrogate model combined with simulation-based methods have been paid much attention in recent years. These techniques can reduce the computational cost to a certain extent. However, the computational burden may still be heavy for complex engineering problems. To address these issues, a Kriging-based important region sampling method is proposed for efficient reliability analysis. The new method is an improvement on the original active learning reliability method combining Kriging and Monte Carlo simulation (AK-MCS), and three strategies are developed to enhance the original method: 1) a new strategy, which is called the key point method, is utilized to define the initial design of experiment (DoE) instead of the Latin hypercube sampling; 2) the concept of dynamic important region/uncertain region and importance factor is proposed to avoid adding useless sample points to the DoE, which have little effect on the accuracy improvement of the Kriging model; 3) the redundant region is introduced to make the distance between the new and existed sample points be larger than a certain value and avoid the information redundancy caused by too close sample points. Five examples are utilized to demonstrate the efficiency and accuracy of this new method.
{"title":"A Kriging-based important region sampling method for efficient reliability analysis","authors":"Junxiang Li, Jianqiao Chen, Jun-hong Wei, Xinhua Yang","doi":"10.1080/16843703.2022.2116265","DOIUrl":"https://doi.org/10.1080/16843703.2022.2116265","url":null,"abstract":"ABSTRACT Reliability analysis methods such as active learning Kriging surrogate model combined with simulation-based methods have been paid much attention in recent years. These techniques can reduce the computational cost to a certain extent. However, the computational burden may still be heavy for complex engineering problems. To address these issues, a Kriging-based important region sampling method is proposed for efficient reliability analysis. The new method is an improvement on the original active learning reliability method combining Kriging and Monte Carlo simulation (AK-MCS), and three strategies are developed to enhance the original method: 1) a new strategy, which is called the key point method, is utilized to define the initial design of experiment (DoE) instead of the Latin hypercube sampling; 2) the concept of dynamic important region/uncertain region and importance factor is proposed to avoid adding useless sample points to the DoE, which have little effect on the accuracy improvement of the Kriging model; 3) the redundant region is introduced to make the distance between the new and existed sample points be larger than a certain value and avoid the information redundancy caused by too close sample points. Five examples are utilized to demonstrate the efficiency and accuracy of this new method.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"360 - 383"},"PeriodicalIF":2.8,"publicationDate":"2022-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46977576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-05DOI: 10.1080/16843703.2022.2109871
Samadrita Bera, N. Jana
ABSTRACT In this paper, we study estimation of stress-strength reliability, assuming the stress and strength variables are inverse Gaussian distributed with unknown parameters. When the coefficient of variations of the distributions is unknown but equal, we develop MLE, Bayes estimator, bootstrap interval of the reliability. The profile likelihood Bayes estimator of the coefficient of variation is also derived. When all parameters are different, we derive the MLE, UMVUE, Bayes estimator, bootstrap interval, and highest posterior density credible interval of the stress-strength reliability. The predictive Bayes estimators of the reliability functions are derived. Under progressive type-II censoring, we derive the MLE, Bayes estimator and bootstrap confidence interval. Monte-Carlo simulation results and real data-based examples are also presented. We analyze lung cancer and air pollution data sets as applications of the stress-strength model.
{"title":"Estimating reliability parameters for inverse Gaussian distributions under complete and progressively type-II censored samples","authors":"Samadrita Bera, N. Jana","doi":"10.1080/16843703.2022.2109871","DOIUrl":"https://doi.org/10.1080/16843703.2022.2109871","url":null,"abstract":"ABSTRACT In this paper, we study estimation of stress-strength reliability, assuming the stress and strength variables are inverse Gaussian distributed with unknown parameters. When the coefficient of variations of the distributions is unknown but equal, we develop MLE, Bayes estimator, bootstrap interval of the reliability. The profile likelihood Bayes estimator of the coefficient of variation is also derived. When all parameters are different, we derive the MLE, UMVUE, Bayes estimator, bootstrap interval, and highest posterior density credible interval of the stress-strength reliability. The predictive Bayes estimators of the reliability functions are derived. Under progressive type-II censoring, we derive the MLE, Bayes estimator and bootstrap confidence interval. Monte-Carlo simulation results and real data-based examples are also presented. We analyze lung cancer and air pollution data sets as applications of the stress-strength model.","PeriodicalId":49133,"journal":{"name":"Quality Technology and Quantitative Management","volume":"20 1","pages":"334 - 359"},"PeriodicalIF":2.8,"publicationDate":"2022-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48130832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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