Bayesian B-Spline Estmation Of The Generalized Varying-Coefficient Models
广义变系数模型的Bayesian B样条估计

This article presents a new approach of estimating generalized varying-coef-ficient models. The functional coefficients are approximated by B-spline functions. We do not select the number of the knots, but use the uniform noninformative prior estimation instead. The prior estimation of the coefficients of the B-spline functions is taken as normal distribution. The functional coefficients are estimated by the methods of the Bayesian model averaging. The advantage of this methods is that the smoothing parameter of each functional coefficient is admitted to be different because of the different posterior estimations of the knot number. In addition, the algorithm of Bayesian B-spline estimation is also given. The simulated examples show that the functional coefficients of the generalized varying-coefficient model are well estimated by Bayesian B-spline methods.