报告内容:
Vector Generalized Linear Models: A Gaussian Copula Approach
Abstract
I will present a multivariate extension to the classic generalized linear models using the approach of the Gaussian copula. This extension furnishes a unified framework of analyzing multi-dimensional nonnormal data in the context of regression analysis. The proposed models can accommodate discrete, continuous and mixed vector outcomes arising in many practical studies, including longitudinal, clustered or spatial data. With the availability of joint probability distributions, we develop a simultaneous maximum likelihood inference in the class of models, where a Gauss-Newton type algorithm is implemented to obtain numerical results for the parameter estimation. I will illustrate vector logistic (or probit) models for multivariate binary outcomes, vector log-linear models for multivariate count outcomes, and a vector GLM for normal and binary mixed outcomes. Comparisons to the GEEs (Generalized Estimating Equations) approach will be discussed in detail through both simulation and data analysis examples.
不好意思,没有翻译出来(水平有限)。也许原汁原味更好吧^_^。