Superconsistent Estimation of Points of Impact in Non-Parametric Regression with Functional Predictors
函数型预测变量非参数回归中影响点的超一致估计
Summary Predicting scalar outcomes by using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time points of the functional predictors have an influence on the outcome. Such ‘points of impact’ are typically unknown and must be estimated in addition to estimating the usual model components. We show that our points-of-impact estimator enjoys a superconsistent rate of convergence and does not…