There are few parameter estimation methods for continuous-discrete mixed distribution,and most of them have the problem of inverse of high-dimensional matrix or low estimation efficiency.In this paper,the minorization-maximization(MM)algorithm and assembly decomposition technique are applied to parameter estimation of Gamma-Poisson mixed distribution.The purpose is to separate and assemble the high-dimensional objective function into a series of linear combinations of low-dimensional functions.Then the difficulty of finding the inverse of high dimensional matrix can be avoided effectively.A series of simulation studies show that MM algorithm and its assembly decomposition have strong accuracy and stability in parameter estimation of Gamma-Poisson mixed distribution model.Applying the Gamma-Poisson mixed distribution model to the data of divorce duration in Belgium,it is found that the Gamma-Poisson mixed distribution model has a good fitting effect on this set of heterogeneous continuous-discrete data.
关键词
Gamma-Poisson混合分布/最小-最大化算法/异质性/比利时离婚数据/组装分解技术
Key words
Gamma-Poisson mixed distribution/minorization-maximization algorithm/heterogeneity/Belgium divorce statistics/assembly and disassembly technique
维普
万方数据