Zhenhua Lin | DeepAI

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Featured Co-authors

Hang Zhou
71 publications
Qiang Sun
39 publications
Hans-Georg Müller
29 publications
Linbo Wang
22 publications
Dehan Kong
21 publications
Jiguo Cao
19 publications
Miles E. Lopes
18 publications
Fang Yao
17 publications
Jane-Ling Wang
13 publications
Yaqing Chen
6 publications
Xiongtao Dai
6 publications

∙ 02/23/2023

Logistic Regression and Classification with non-Euclidean Covariates

We introduce a logistic regression model for data pairs consisting of a ...

0 Yinan Lin, et al. ∙

∙ 08/18/2022

Optimal One-pass Nonparametric Estimation Under Memory Constraint

For nonparametric regression in the streaming setting, where data consta...

0 Mingxue Quan, et al. ∙

∙ 09/06/2021

A Unified Approach to Hypothesis Testing for Functional Linear Models

We develop a unified approach to hypothesis testing for various types of...

0 Yinan Lin, et al. ∙

∙ 05/31/2021

Intrinsic Wasserstein Correlation Analysis

We develop a framework of canonical correlation analysis for distributio...

0 Hang Zhou, et al. ∙

∙ 01/05/2021

Causal Inference on Non-linear Spaces: Distribution Functions and Beyond

Understanding causal relationships is one of the most important goals of...

0 Zhenhua Lin, et al. ∙

∙ 09/18/2020

Additive Models for Symmetric Positive-Definite Matrices, Riemannian Manifolds and Lie groups

In this paper an additive regression model for a symmetric positive-defi...

0 Zhenhua Lin, et al. ∙

∙ 09/16/2020

Intrinsic Riemannian Functional Data Analysis for Sparse Longitudinal Observations

A novel framework is developed to intrinsically analyze sparsely observe...

0 Zhenhua Lin, et al. ∙

∙ 07/02/2020

High-dimensional MANOVA via Bootstrapping and its Application to Functional and Sparse Count Data

We propose a new approach to the problem of high-dimensional multivariat...

0 Zhenhua Lin, et al. ∙

∙ 06/17/2020

Wasserstein Regression

The analysis of samples of random objects that do not lie in a vector sp...

0 Yaqing Chen, et al. ∙

∙ 06/05/2020

Mean and Covariance Estimation for Functional Snippets

We consider estimation of mean and covariance functions of functional sn...

0 Zhenhua Lin, et al. ∙

∙ 08/25/2019

Riemannian Geometry of Symmetric Positive Definite Matrices via Cholesky Decomposition

We present a new Riemannian metric, termed Log-Cholesky metric, on the m...

0 Zhenhua Lin, et al. ∙

∙ 07/08/2019

Modeling Symmetric Positive Definite Matrices with An Application to Functional Brain Connectivity

In neuroscience, functional brain connectivity describes the connectivit...

0 Zhenhua Lin, et al. ∙

∙ 05/16/2019

Basis Expansions for Functional Snippets

Estimation of mean and covariance functions is fundamental for functiona...

0 Zhenhua Lin, et al. ∙

∙ 05/16/2019

Analytic Basis Expansions for Functional Snippets

Estimation of mean and covariance functions is fundamental for functiona...

0 Zhenhua Lin, et al. ∙

∙ 04/21/2019

Total Variation Regularized Fréchet Regression for Metric-Space Valued Data

Non-Euclidean data that are indexed with a scalar predictor such as time...

0 Zhenhua Lin, et al. ∙

∙ 12/12/2018

Modeling Longitudinal Data on Riemannian Manifolds

When considering functional principal component analysis for sparsely ob...

0 Xiongtao Dai, et al. ∙

∙ 12/05/2018

Intrinsic Riemannian Functional Data Analysis

In this work we develop a novel and foundational framework for analyzing...

0 Zhenhua Lin, et al. ∙

∙ 09/13/2018

Estimating Historical Functional Linear Models with a Nested Group Bridge Approach

We study a scalar-on-function historical linear regression model which a...

0 Tianyu Guan, et al. ∙

∙ 07/12/2018

Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional Data Analysis

In recent years, bootstrap methods have drawn attention for their abilit...

0 Miles E. Lopes, et al. ∙

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