将广义变系数回归模型与广义函数型线性回归模型相结合, 提出了一种新的模型------广义函数型部分变系数混合模型. 基于函数型主成分基和~B-样条基的方法, 通过最大化拟似然函数得到了未知函数的估计, 并在一定的正则条件下得到了各估计量的收敛速度及预测精度. 通过数值模拟展现了模型的可行性和优越性, 最后将所建模型应用到~Tecator 数据说明了模型的实用性.

In this paper, a novel generalized functional partially varying coefficient hybrid models is proposed, which combines the generalized varying coefficient regression model with the generalized functional linear regression model. Based on the method of functional principal component base and B-splines base, the estimation of unknown functions is obtained by maximizing quasi-likelihood function, and the convergence rate and prediction accuracy of each estimator are obtained under certain regular conditions. The feasibility and superiority of the model are demonstrated by numerical simulation, and the practicability of the model is illustrated by applying the model to Tecator data.