35 citations · 72 across the 8 of their papers we have counts for
9 papers
Test of Weak Separability for Spatially Stationary Functional Field
Decai Liang, Hui Huang, Yongtao Guan +1
For spatially dependent functional data, a generalized Karhunen-Loève expansion is commonly used to decompose data into an additive form of temporal components and spatially correl…
Online Estimation for Functional Data
Ying Yang, Fang Yao
Functional data analysis has attracted considerable interest and is facing new challenges, one of which is the increasingly available data in a streaming manner. In this article we…
Intrinsic Wasserstein Correlation Analysis
Hang Zhou, Zhenhua Lin, Fang Yao
We develop a framework of canonical correlation analysis for distribution-valued functional data within the geometry of Wasserstein spaces. Specifically, we formulate an intrinsic…
Sparse Functional Principal Component Analysis in High Dimensions
Xiaoyu Hu, Fang Yao
Functional principal component analysis (FPCA) is a fundamental tool and has attracted increasing attention in recent decades, while existing methods are restricted to data with a…
Nonparametric principal subspace regression
Mark Koudstaal, Dengdeng Yu, Dehan Kong +1
In scientific applications, multivariate observations often come in tandem with temporal or spatial covariates, with which the underlying signals vary smoothly. The standard approa…
Distribution and correlation free two-sample test of high-dimensional means
Kaijie Xue, Fang Yao
We propose a two-sample test for high-dimensional means that requires neither distributional nor correlational assumptions, besides some weak conditions on the moments and tail pro…