Statistical Modeling Of Several Types Of Mixed Data And Its Applicatio

In recent years,statistical modeling of multivariable count data with covariance structure has attracted more and more attention.In the study of multi-variable count data,the different types of count data will lead to a variety of different data structures in the target study data.However,such complex data structures can make the estimation and interpretation process of the model very cumbersome.Therefore,this paper mainly studies the correlation structure between different data types,introduces multivariate random effects with flexible covariance structure,and proposes a new multivariate count data model,which is extended to Tweedie model.Multiple random effects with flexible covariance structure can be used to adjust and estimate unstructured covariance.The model established in this paper can be interpreted,and the multivariate random effect directly represents the potential strength between multivariate count data.In addition,the method also consolidates the conditional and marginal modeling interpretation.Tweedie model with multiple random effects can be widely applied in epidemiology,actuarial science,insurance and other industries.Using Tweedie model with multiple random effects can well solve problems encountered in real life.This paper establishes a Tweedie model of multiple random effects with unstructured covariance.The main research contents are as follows:1.Discuss the manifestation form of Tweedie model,introduce multiple random effects with unstructured covariance structure into Tweedie model,and build a Tweedie model with multiple random effects with unstructured covariance.2.Optimal linear unbiased prediction is used for estimation.The unknown parameters are estimated by Newton-Raphson iteration method and moment estimation method.3.The Tweedie model with multiple random effects established in this paper was used to illustrate the method through case analysis of osteojoint initiative data and ischemic heart disease data.Osteojoint Initiative data are a mixture of semi-continuous data and discrete data.Ischemic heart disease was studied with a mixture of discrete,semi-continuous and right-skewed data.Different data types are analyzed,and the performance of the method is evaluated.