研究期間：10108~10207;The aim of this project is to establish a new semi-parametric means of analyzing correlated data. The idea is to combine two estimating equations, one for dependent data and one for accommodating the nature of within-cluster association existing in data. This project is inspired by the work of Tsou (2008a, 2008b), where the multivariate negative binomial model was used to analyze general correlated count data. The score function based on this working model is composed of two parts. The first one is simply a score function from Poisson and the second is an estimating function utilizing cluster total as response. Both functions are unbiased. We explore the usefulness of this “composite estimating equations＂ method for analyzing correlated data whose underlying distributions need not to be known. The performance of the composite estimating equations will be investigated in terms of asymptotic properties, such as the asymptotic normality and the efficiency. Comparisons to the generalized estimating equations (GEEs) and the composite likelihood method will also be made.
