Robust functional regression with discretely sampled predictors
针对离散采样函数数据，提出一类基于薄板样条和新型二次惩罚的稳健函数回归估计量，理论证明样本量和离散化误差共同决定收敛速度，模拟和实例验证了有效性。
The functional linear model is an important extension of the classical regression model allowing for scalar responses to be modeled as functions of stochastic processes. Yet, despite the usefulness and popularity of the functional linear model in recent years, most treatments, theoretical and practical alike, suffer either from (i) lack of resistance towards the many types of anomalies one may encounter with functional data or (ii) biases resulting from the use of discretely sampled functional data instead of completely observed data. To address these deficiencies, the first class of robust functional regression estimators for partially observed functional data is introduced and studied. The proposed broad class of estimators is based on thin-plate splines with a novel, computationally efficient quadratic penalty, is easily implementable and enjoys good theoretical properties under weak assumptions. It is shown that, in the incomplete data setting, both the sample size and discretization error of the processes determine the asymptotic rate of convergence of functional regression estimators and the latter cannot be ignored. These theoretical properties remain valid even with multi-dimensional random fields acting as predictors and random smoothing parameters. The effectiveness of the proposed class of estimators in practice is demonstrated by means of a simulation study and a real-data example.