Kernel Quadratic Inference Function Method for Varying coefficient Model

Abstract:

Following the idea of the quadratic inference function (QIF), a kernel quadratic function method for varying coefficient model with longitudinal data was proposed by using local polynomial smoothing method and approximating the working correlation with a serious of basic matrices in the generalized estimation equation. The asymptotic normality of the estimators of the coefficient functions was proved. This method improved the performance of the estimators by widening the bandwidth in order to plug in the correlation within subjects to the local area, which won’t lead to an “over fitting” phenomenon. An applied method was also proposed to choose QIF bandwidth in the simulation.