720 citations · 868 across the 21 of their papers we have counts for
21 papers
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…
Deep Regression for Repeated Measurements
Shunxing Yan, Fang Yao, Hang Zhou
Nonparametric mean function regression with repeated measurements serves as a cornerstone for many statistical branches, such as longitudinal/panel/functional data analysis. In thi…
Theory of functional principal component analysis for discretely observed data
Hang Zhou, Dongyi Wei, Fang Yao
Functional data analysis is an important research field in statistics which treats data as random functions drawn from some infinite-dimensional functional space, and functional pr…
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption
Lihu Xu, Fang Yao, Qiuran Yao +1
There has been a surge of interest in developing robust estimators for models with heavy-tailed and bounded variance data in statistics and machine learning, while few works impose…
Online Generalized Additive Model
Ying Yang, Fang Yao
Additive models and generalized additive models are effective semiparametric tools for multidimensional data. In this article we propose an online smoothing backfitting method for…
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,…
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…
Intrinsic Riemannian Functional Data Analysis
Zhenhua Lin, Fang Yao
In this work we develop a novel and foundational framework for analyzing general Riemannian functional data, in particular a new development of tensor Hilbert spaces along curves o…
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…
Empirical dynamics for longitudinal data
Hans-Georg Müller, Fang Yao
We demonstrate that the processes underlying on-line auction price bids and many other longitudinal data can be represented by an empirical first order stochastic ordinary differen…
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…
Estimation of functional derivatives
Peter Hall, Hans-Georg Müller, Fang Yao
Situations of a functional predictor paired with a scalar response are increasingly encountered in data analysis. Predictors are often appropriately modeled as square integrable sm…
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)…
Functional linear regression analysis for longitudinal data
Fang Yao, Hans-Georg Müller, Jane-Ling Wang
We propose nonparametric methods for functional linear regression which are designed for sparse longitudinal data, where both the predictor and response are functions of a covariat…