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05/25/2023
Robust Functional Data Analysis for Discretely Observed Data
This paper examines robust functional data analysis for discretely obser...
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05/17/2023
Dynamic Matrix Recovery
Matrix recovery from sparse observations is an extensively studied topic...
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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...
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02/27/2023
Nonparametric regression for repeated measurements with deep neural networks
Analysis of repeated measurements for a sample of subjects has been inte...
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09/19/2022
Theory of functional principal components analysis for discretely observed data
For discretely observed functional data, estimating eigenfunctions with ...
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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...
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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...
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12/17/2021
Online Generalized Additive Model
Additive models and generalized additive models are effective semiparame...
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11/05/2021
Test of Weak Separability for Spatially Stationary Functional Field
For spatially dependent functional data, a generalized Karhunen-Loève ex...
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11/04/2021
Online Estimation for Functional Data
Functional data analysis has attracted considerable interest and is faci...
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05/31/2021
Intrinsic Wasserstein Correlation Analysis
We develop a framework of canonical correlation analysis for distributio...
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04/07/2021
Dynamic Principal Subspaces with Sparsity in High Dimensions
Principal component analysis (PCA) is a versatile tool to reduce the dim...
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11/02/2020
Sparse Functional Principal Component Analysis in High Dimensions
Functional principal component analysis (FPCA) is a fundamental tool and...
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09/16/2020
Intrinsic Riemannian Functional Data Analysis for Sparse Longitudinal Observations
A novel framework is developed to intrinsically analyze sparsely observe...
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10/07/2019
Nonparametric principal subspace regression
In scientific applications, multivariate observations often come in tand...
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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...
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12/05/2018