research
∙
05/25/2023
Robust Functional Data Analysis for Discretely Observed Data
This paper examines robust functional data analysis for discretely obser...
share
research
∙
05/17/2023
Dynamic Matrix Recovery
Matrix recovery from sparse observations is an extensively studied topic...
research
∙
05/14/2023
Binary and Re-search Signal Region Detection in High Dimensions
Signal region detection is one of the challenging problems in modern sta...
research
∙
02/27/2023
Nonparametric regression for repeated measurements with deep neural networks
Analysis of repeated measurements for a sample of subjects has been inte...
research
∙
09/19/2022
Theory of functional principal components analysis for discretely observed data
For discretely observed functional data, estimating eigenfunctions with ...
research
∙
02/22/2022
Policy Evaluation for Temporal and/or Spatial Dependent Experiments in Ride-sourcing Platforms
Policy evaluation based on A/B testing has attracted considerable intere...
research
∙
01/10/2022
Non-Asymptotic Guarantees for Robust Statistical Learning under (1+ε)-th Moment Assumption
There has been a surge of interest in developing robust estimators for m...
research
∙
12/17/2021
Online Generalized Additive Model
Additive models and generalized additive models are effective semiparame...
research
∙
11/05/2021
Test of Weak Separability for Spatially Stationary Functional Field
For spatially dependent functional data, a generalized Karhunen-Loève ex...
research
∙
11/04/2021
Online Estimation for Functional Data
Functional data analysis has attracted considerable interest and is faci...
research
∙
05/31/2021
Intrinsic Wasserstein Correlation Analysis
We develop a framework of canonical correlation analysis for distributio...
research
∙
04/07/2021
Dynamic Principal Subspaces with Sparsity in High Dimensions
Principal component analysis (PCA) is a versatile tool to reduce the dim...
research
∙
11/02/2020
Sparse Functional Principal Component Analysis in High Dimensions
Functional principal component analysis (FPCA) is a fundamental tool and...
research
∙
09/16/2020
Intrinsic Riemannian Functional Data Analysis for Sparse Longitudinal Observations
A novel framework is developed to intrinsically analyze sparsely observe...
research
∙
10/07/2019
Nonparametric principal subspace regression
In scientific applications, multivariate observations often come in tand...
research
∙
04/16/2019
Distribution and correlation free two-sample test of high-dimensional means
We propose a two-sample test for high-dimensional means that requires ne...
research
∙
12/05/2018
