110 citations · 269 across the 19 of their papers we have counts for
20 papers
Binary Regression and Classification with Covariates in Metric Spaces
Yinan Lin, Zhenhua Lin
Inspired by logistic regression, we introduce a regression model for data tuples consisting of a binary response and a set of covariates residing in a metric space without vector s…
Optimal One-pass Nonparametric Estimation Under Memory Constraint
Mingxue Quan, Zhenhua Lin
For nonparametric regression in the streaming setting, where data constantly flow in and require real-time analysis, a main challenge is that data are cleared from the computer sys…
Hypothesis Testing for Functional Linear Models via Bootstrapping
Yinan Lin, Zhenhua Lin
Hypothesis testing for the slope function in functional linear regression is of both practical and theoretical interest. We develop a novel test for the nullity of the slope functi…
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…
Causal Inference on Distribution Functions
Zhenhua Lin, Dehan Kong, Linbo Wang
Understanding causal relationships is one of the most important goals of modern science. So far, the causal inference literature has focused almost exclusively on outcomes coming f…
Additive Models for Symmetric Positive-Definite Matrices, Riemannian Manifolds and Lie groups
Zhenhua Lin, Hans-Georg Müller, Byeong U. Park
In this paper an additive regression model for a symmetric positive-definite matrix valued response and multiple scalar predictors is proposed. The model exploits the abelian group…
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,…
High-dimensional MANOVA via Bootstrapping and its Application to Functional and Sparse Count Data
Zhenhua Lin, Miles E. Lopes, Hans-Georg Müller
We propose a new approach to the problem of high-dimensional multivariate ANOVA via bootstrapping max statistics that involve the differences of sample mean vectors. The proposed m…
Mean and Covariance Estimation for Functional Snippets
Zhenhua Lin, Jane-Ling Wang
We consider estimation of mean and covariance functions of functional snippets, which are short segments of functions possibly observed irregularly on an individual specific subint…
Wasserstein Regression
Yaqing Chen, Zhenhua Lin, Hans-Georg Müller
The analysis of samples of random objects that do not lie in a vector space is gaining increasing attention in statistics. An important class of such object data is univariate prob…
Riemannian Geometry of Symmetric Positive Definite Matrices via Cholesky Decomposition
Zhenhua Lin
We present a new Riemannian metric, termed Log-Cholesky metric, on the manifold of symmetric positive definite (SPD) matrices via Cholesky decomposition. We first construct a Lie g…
Modeling Symmetric Positive Definite Matrices with An Application to Functional Brain Connectivity
Zhenhua Lin, Dehan Kong, Qiang Sun
In neuroscience, functional brain connectivity describes the connectivity between brain regions that share functional properties. Neuroscientists often characterize it by a time se…
Basis Expansions for Functional Snippets
Zhenhua Lin, Jane-Ling Wang, Qixian Zhong
Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that t…
Total Variation Regularized Fréchet Regression for Metric-Space Valued Data
Zhenhua Lin, Hans-Georg Müller
Non-Euclidean data that are indexed with a scalar predictor such as time are increasingly encountered in data applications, while statistical methodology and theory for such random…
Modeling Longitudinal Data on Riemannian Manifolds
Xiongtao Dai, Zhenhua Lin, Hans-Georg Müller
When considering functional principal component analysis for sparsely observed longitudinal data that take values on a nonlinear manifold, a major challenge is how to handle the sp…
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…
Estimating Historical Functional Linear Models with a Nested Group Bridge Approach
Tianyu Guan, Zhenhua Lin, Jiguo Cao
We study a scalar-on-function historical linear regression model which assumes that the functional predictor does not influence the response when the time passes a certain cutoff p…
Bootstrapping Max Statistics in High Dimensions: Near-Parametric Rates Under Weak Variance Decay and Application to Functional and Multinomial Data
Miles E. Lopes, Zhenhua Lin, Hans-Georg Mueller
In recent years, bootstrap methods have drawn attention for their ability to approximate the laws of "max statistics" in high-dimensional problems. A leading example of such a stat…
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…
A Smooth and Locally Sparse Estimator for Functional Linear Regression via Functional SCAD Penalty
Zhenhua Lin, Jiguo Cao, Liangliang Wang +1
In this paper, we propose a new regularization technique called "functional SCAD". We then combine this technique with the smoothing spline method to develop a smooth and locally s…