Nonparametric estimation via partial derivatives
提出利用偏导数信息进行非参数估计的方法，能在高维数据中达到接近参数化的收敛速度，避免维数灾难，适用于经济学、计算机科学等领域。
Abstract Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either observed or estimated, to effectively estimate the function at near-parametric convergence rates. This novel approach and computational algorithm could lead to methods useful to practitioners in many areas of science and engineering. Our theoretical results reveal behaviour universal to this class of nonparametric estimation problems. We explore a general setting involving tensor product spaces and build upon the smoothing spline analysis of variance framework. For d-dimensional models under full interaction, the optimal rates with gradient information on p covariates are identical to those for the (d−p)-interaction models without gradients and, therefore, the models are immune to the curse of interaction. For additive models, the optimal rates using gradient information are n, thus achieving the parametric rate. We demonstrate aspects of the theoretical results through synthetic and real data applications.