联合均值和散度逆高斯回归模型的参数估计OA北大核心CHSSCDCSSCICSTPCD

Inverse Gaussian regression models can be used to analyze positive skewness data.People usually study the influ-ence of explanatory variables on their mean parameters,but often ignore the influence on their dispersion parameters.Based on the premise that explanatory variables have an effect on both mean and dispersion,this paper discusses the maximum likelihood esti-mation of model parameters for the joint mean and inverse Gaussian regression model of dispersion.In terms of optimization,MM algorithm has the advantages of separating parameters,reducing the dimension of objective function,simplifying the solving pro-cess and so on.MM algorithm applied to joint mean and the inverse Gaussian regression model of dispersion,the multivariate like-lihood function can be completely decomposed into the sum of a series of unary functions,thus bypassing matrix inversion in pa-rameter estimation.Simulation studies show that good estimation effect can be obtained when the amount of data reaches 100.The empirical analysis shows that the theoretical research is feasible in practical application.