基于高斯伪似然的正定相关阵估计及其应用

Positive Definite Correlation Matrix Estimator and Its Application Based on Gaussian Pseudo-likelihood

基于高斯伪似然方法提出一种广义估计方程中工作相关阵的相合正定估计方法,并证明了无论工作相关阵是否被正确指定,所提出的AR(1)、等相关和MA(1)工作相关阵的高斯伪似然估计均为正定的.模拟结果表明,基于正定相关阵估计广义估计方程的回归参数估计是高效的.

Based on a Gaussian pseudo-likelihood method,we proposed a methd to estimate the consistency of the positive definite correlation matrix of generalized estimating equations.We proved that the Gaussian pseudo-likelihood estimators of AR（1）,exchangeable and MA（1）working correlation matrices were positive definite,even if the correlation matrix was misspecified.Simulation result shows that the regression parameter estimator of the generalized estimating equations based on the positive definite correlation matrix estimator is efficient.