720 citations · 868 across the 21 of their papers we have counts for
12 papers · 1 filter
Robust Functional Data Analysis for Discretely Observed Data
Lingxuan Shao, Fang Yao
This paper examines robust functional data analysis for discretely observed data, where the underlying process encompasses various distributions, such as heavy tail, skewness, or c…
Dynamic Matrix Recovery
Ziyuan Chen, Ying Yang, Fang Yao
Matrix recovery from sparse observations is an extensively studied topic emerging in various applications, such as recommendation system and signal processing, which includes the m…
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…
Dynamic Principal Component Analysis in High Dimensions
Xiaoyu Hu, Fang Yao
Principal component analysis is a versatile tool to reduce dimensionality which has wide applications in statistics and machine learning. It is particularly useful for modeling dat…
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…
Intrinsic Riemannian Functional Data Analysis for Sparse Longitudinal Observations
Lingxuan Shao, Zhenhua Lin, Fang Yao
A new framework is developed to intrinsically analyze sparsely observed Riemannian functional data. It features four innovative components: a frame-independent covariance function,…
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…
Functional Regression on Manifold with Contamination
Zhenhua Lin, Fang Yao
We propose a new method for functional nonparametric regression with a predictor that resides on a finite-dimensional manifold but is only observable in an infinite-dimensional spa…
Statistical Testing for Conditional Copulas
Elif F. Acar, Radu V. Craiu, Fang Yao
In conditional copula models, the copula parameter is deterministically linked to a covariate via the calibration function. The latter is of central interest for inference and is u…
Nonparametric Covariate Adjustment for Receiver Operating Characteristic Curves
Fang Yao, Radu V. Craiu, Benjamin Reiser
The accuracy of a diagnostic test is typically characterised using the receiver operating characteristic (ROC) curve. Summarising indexes such as the area under the ROC curve (AUC)…