Dependent competing risks model is a practical model in the analysis of lifetime and failure modes.The dependence can be captured using a statistical tool to explore the re-lationship among failure causes.In this pape...Dependent competing risks model is a practical model in the analysis of lifetime and failure modes.The dependence can be captured using a statistical tool to explore the re-lationship among failure causes.In this paper,an Archimedean copula is chosen to describe the dependence in a constant-stress accelerated life test.We study the Archimedean copula based dependent competing risks model using parametric and nonparametric methods.The parametric likelihood inference is presented by deriving the general expression of likelihood function based on assumed survival Archimedean copula associated with the model parameter estimation.Combining the nonparametric estimation with progressive censoring and the non-parametric copula estimation,we introduce a nonparametric reliability estimation method given competing risks data.A simulation study and a real data analysis are conducted to show the performance of the estimation methods.展开更多
The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the proble...The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.展开更多
This paper considers series and parallel systems comprising n components drawn from a heterogeneous population consisting of m different subpopulations.The components within each subpopulation are assumed to be depend...This paper considers series and parallel systems comprising n components drawn from a heterogeneous population consisting of m different subpopulations.The components within each subpopulation are assumed to be dependent,while the subpopulations are independent of each other.The authors also assume that the subpopulations have different Archimedean copulas for their dependence.Under this setup,the authors discuss the series and parallel systems reliability for three different cases,respectively.The authors use the theory of stochastic orders and majorization to establish the main results,and finally present some numerical examples to illustrate all the results established here.展开更多
基金Supported by the National Natural Science Foundation of China(12101476,12061091,11901134)the Fundamental Research Funds for the Central Universities(ZYTS23054,QTZX22054)+1 种基金the Yunnan Funda-mental Research Projects(202101AT070103)the Natural Science Basic Research Program of Shaanxi Province(2020JQ-285).
文摘Dependent competing risks model is a practical model in the analysis of lifetime and failure modes.The dependence can be captured using a statistical tool to explore the re-lationship among failure causes.In this paper,an Archimedean copula is chosen to describe the dependence in a constant-stress accelerated life test.We study the Archimedean copula based dependent competing risks model using parametric and nonparametric methods.The parametric likelihood inference is presented by deriving the general expression of likelihood function based on assumed survival Archimedean copula associated with the model parameter estimation.Combining the nonparametric estimation with progressive censoring and the non-parametric copula estimation,we introduce a nonparametric reliability estimation method given competing risks data.A simulation study and a real data analysis are conducted to show the performance of the estimation methods.
基金supported by the National Natural Science Foundation of China(61201370)the Special Funding Project for Independent Innovation Achievement Transform of Shandong Province(2012CX30202)the Natural Science Foundation of Shandong Province(ZR2014FM039)
文摘The artificial bee colony （ABC） algorithm is a com- petitive stochastic population-based optimization algorithm. How- ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in- sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm （ACEDA） is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA called Archimedean copula estima- tion of distribution based on the artificial bee colony （ACABC） algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench- mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen- tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best （gbest）-guided ABC （GABC） algorithm in most of the experiments.
基金supported by the National Natural Science Foundation of China under Grant No.11971116the Anhui Provincial Natural Science Foundation under Grant No.1808085MA03the PhD research startup foundation of Anhui Normal University under Grant No.2014bsqdjj34。
文摘This paper considers series and parallel systems comprising n components drawn from a heterogeneous population consisting of m different subpopulations.The components within each subpopulation are assumed to be dependent,while the subpopulations are independent of each other.The authors also assume that the subpopulations have different Archimedean copulas for their dependence.Under this setup,the authors discuss the series and parallel systems reliability for three different cases,respectively.The authors use the theory of stochastic orders and majorization to establish the main results,and finally present some numerical examples to illustrate all the results established here.
