Showing math.STShow all
3 papers · 1 filter
math.ST2022
Functional sufficient dimension reduction through distance covariance
Xing Yang, Jianjun Xu
Our research proposes a novel method for reducing the dimensionality of functional data, specifically for the case where the response is a scalar and the predictor is a random func…
math.ST2020
A new RKHS-based global testing for functional linear model
Jianjun Xu, Wenquan Cui
This article studies global testing of the slope function in functional linear regression model in the framework of reproducing kernel Hilbert space. We propose a new testing stati…
math.ST2019
A new reproducing kernel based nonlinear dimension reduction method for survival data
Wenquan Cui, Jianjun Xu, Yuehua Wu
Based on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS-based Double SIR) to nonlinear dimension reduction…