A Tensor Estimation Approach to Multivariate Additive Models
报 告 人:: 刘旭
报告地点:: 数学与统计学院415报告厅
报告时间:: 2018年11月06日星期二09:30-10:30
报告简介:

We consider parsimonious modeling of high-dimensional multivariate additive models (MAM) using regression splines, with or without sparsity assumptions. The approach is based on treating the coefficients as a third-order tensor and a Tucker decomposition is used to reduce the number of parameters in the tensor. The method can avoid the statistical inefficiency caused by estimating a large number of nonparametric functions. We establish the convergence rate of the proposed estimator. Numerical examples are presented to demonstrate the advantages of the proposed novel approach.

举办单位:数学与统计学院
发 布 人:吴双 发布时间: 2018-11-06
主讲人简介:
2011年博士毕业与云南大学,现在为上海财经大学统计与管理学院助理教授。研究兴趣为高维数据和基因数据分析,以及非参半参数统计建模。