地 点: 理科大楼A1514室
题 目：Uniform design motivated basis selection methods for smoothing spline regression
Fitting a smoothing spline model on a large-scale dataset is daunting due to the high computational cost. The basis selection methods for smoothing spline calculation are regarded as an efficient way to deal with the large-scale dataset. The key to success is to force a non-parametric function in an infinite-dimensional functional space to reside in a relatively small and finite-dimensional model space without the loss of too much prediction accuracy. Space-filling basis selection is proven more efficient among various basis selection methods since the dimension of its model space is smaller than others. In this talk, we illustrate two efficient space-filling basis selection methods for smoothing spline calculation. The key idea is to adopt a uniform design to the large-scale dataset and use projective uniformity to improve the statistical efficiency when the underlying response surface is not isomorphic. It is proved that the illustrated estimator has the same convergence rate as the full-basis estimator. Compared with the standard approach, the proposed method significantly reduce the computational cost.
北京理工大学助理教授，博士生导师。本科毕业于南开大学、博士毕业于北京大学，曾在美国佐治亚州立大学作访问学者。主要从事试验设计、大数据抽样相关研究，在Journal of the American Statistical Association、Statistica Sinica, NeurIPS等顶级期刊、会议发表多篇学术论文。