纵向零膨胀计数数据的多参数联合回归模型OA

Longitudinal zero-inflated count data are gathered from a series of measurements of experimental individuals at multiple time points and possess the very characteristics of longitudinal data,count data and zero-inflated data simultaneously.The number of zeros contained in count data far exceeds that randomly gen-erated by classical discrete distributions such as Poisson and negative binomial distributions.In the regression analysis of longitudinal zero-inflated count data,the data are generally assumed to consist of zero-inflated com-ponents and random sampling components,and the mean,zero-inflation rate and dispersion are three main characteristic parameters of the data.Nowadays,most regression models for zero-inflated data only consider the influence of covariates on the mean and zero-inflation rate and the dispersion is unfortunately igored or set to a fixed value.As a result,these models cannot be applied to the situations where dispersion at different ob-servation time points dynamically changes with time or other covariates.For the count data following the zero-inflated negative binomial distribution,a generalized regression model is develpoed to describe the relation-ship between all three data characteristics and the covariates.The maximum likelihood estimate of the regres-sion coefficients is obtained by using the EM(Expectation-Maximization)algorithm to simultaneously the three regression coefficients.Theoretical analysis shows that,in comparison with the two-parameter regres-sion model estimation,the proposed likelihood estimator is consistent and asymptotically normal,enabling ac-curate simultaneous estimation of the three regression coefficients.Finally,simulation results demonstrate that the proposed model is more accurate and effective than the regression models that do not consider time-varying dispersion.