The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high...The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high,it usually brings challenges to the parameter es-timation of the mixed distribution model.The application of MM algorithm can avoid complex expectation calculations,and can also solve the problem of high-dimensional optimization by decomposing the objective function.In this paper,MM algorithm is applied to the parameter estimation problem of mixed distribution model.The method of assembly and decomposition is used to construct the substitute function with separable parameters,which avoids the problems of complex expectation calculations and the inversion of high-dimensional matrices.展开更多
During maintainability demonstration,the maintenance time for complex systems consisting of mixed technologies generally conforms to a mixture distribution.However existing maintainability standards and guidance do no...During maintainability demonstration,the maintenance time for complex systems consisting of mixed technologies generally conforms to a mixture distribution.However existing maintainability standards and guidance do not explain explicitly how to deal with this situation.This paper develops a comprehensive maintainability demonstration method for complex systems with a mixed maintenance time distribution.First of all,a K-means algorithm and an expectation-maximization(EM)algorithm are used to partition the maintenance time data for all possible clusters.The Bayesian information criterion(BIC)is then used to choose the optimal model.After this,the clustering results for equipment are obtained according to their degree of membership.The degree of similarity for the maintainability of different kinds of equipment is then determined using the projection method.By using a Bootstrap method,the prior distribution is obtained from the maintenance time data for the most similar equipment.Then,a test method based on Bayesian theory is outlined for the maintainability demonstration.Finally,the viability of the proposed approach is illustrated by means of an example.展开更多
Medical insurance service, the important part of national healthcare supporting system with a history dating back more than 100 years ago, remains a global challenge because of its high rates of compensation and diffi...Medical insurance service, the important part of national healthcare supporting system with a history dating back more than 100 years ago, remains a global challenge because of its high rates of compensation and difficulty in risk control. When developing the diabetes related, hospitalization insurance, we found that the risk loss of the diabetic inpatients does not follow a symmetrical unimodal distribution: in fact, it is hard to describe its risk loses distribution with a single probability distribution model. Therefore, we put forward a risk measurement method based on a mixed normal distributions model for medical insurance of inpatients with diabetes.展开更多
基金Supported by the National Natural Science Foundation of China(12261108)the General Program of Basic Research Programs of Yunnan Province(202401AT070126)+1 种基金the Yunnan Key Laboratory of Modern Analytical Mathematics and Applications(202302AN360007)the Cross-integration Innovation team of modern Applied Mathematics and Life Sciences in Yunnan Province,China(202405AS350003).
文摘The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high,it usually brings challenges to the parameter es-timation of the mixed distribution model.The application of MM algorithm can avoid complex expectation calculations,and can also solve the problem of high-dimensional optimization by decomposing the objective function.In this paper,MM algorithm is applied to the parameter estimation problem of mixed distribution model.The method of assembly and decomposition is used to construct the substitute function with separable parameters,which avoids the problems of complex expectation calculations and the inversion of high-dimensional matrices.
基金supported by the National Defense Pre-research Funds(9140A27010215JB34422)
文摘During maintainability demonstration,the maintenance time for complex systems consisting of mixed technologies generally conforms to a mixture distribution.However existing maintainability standards and guidance do not explain explicitly how to deal with this situation.This paper develops a comprehensive maintainability demonstration method for complex systems with a mixed maintenance time distribution.First of all,a K-means algorithm and an expectation-maximization(EM)algorithm are used to partition the maintenance time data for all possible clusters.The Bayesian information criterion(BIC)is then used to choose the optimal model.After this,the clustering results for equipment are obtained according to their degree of membership.The degree of similarity for the maintainability of different kinds of equipment is then determined using the projection method.By using a Bootstrap method,the prior distribution is obtained from the maintenance time data for the most similar equipment.Then,a test method based on Bayesian theory is outlined for the maintainability demonstration.Finally,the viability of the proposed approach is illustrated by means of an example.
基金This study was granted by Guangdong Province Medical Science Research Fund (No. A2002255)
文摘Medical insurance service, the important part of national healthcare supporting system with a history dating back more than 100 years ago, remains a global challenge because of its high rates of compensation and difficulty in risk control. When developing the diabetes related, hospitalization insurance, we found that the risk loss of the diabetic inpatients does not follow a symmetrical unimodal distribution: in fact, it is hard to describe its risk loses distribution with a single probability distribution model. Therefore, we put forward a risk measurement method based on a mixed normal distributions model for medical insurance of inpatients with diabetes.