Yen-Chi Chen

ASA Noether Early Career Scholar Award, American Statistical Association (ASA) (2022)

Umesh K. Gavasakar Thesis Award, Carneige Mellon University (2017)

William S. Dietrich II Presidential Ph.D. Fellowship Award, Carneige Mellon University (2015)

CAREER Award, National Science Foundation (2022-2027)

Nonparametric Inference on Dose-Response Curves Without the Positivity Condition
Yikun Zhang, Yen-Chi Chen, Alexander Giessing
Existing statistical methods in causal inference often rely on the assumption that every individual has some chance of receiving any treatment level regardless…

Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data
Yikun Zhang, Yen-Chi Chen
This paper studies the linear convergence of the subspace constrained mean shift (SCMS) algorithm, a well-known algorithm for identifying a density ridge…

Statistical Inference with Local Optima
Yen-Chi Chen
We study the statistical properties of an estimator derived by applying a gradient ascent method with multiple initializations to a multi-modal likelihood…

Solution manifold and Its Statistical Applications
Yen-Chi Chen
A solution manifold is the collection of points in a $d$-dimensional space satisfying a system of $s$ equations with $s

Pattern graphs: a graphical approach to nonmonotone missing data
Yen-Chi Chen
We introduce the concept of pattern graphs--directed acyclic graphs representing how response patterns are associated. A pattern graph represents an…

Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data
Yikun Zhang, Yen-Chi Chen
Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental…

The EM Perspective of Directional Mean Shift Algorithm
Yikun Zhang, Yen-Chi Chen
The directional mean shift (DMS) algorithm is a nonparametric method for pursuing local modes of densities defined by kernel density estimators on the unit…

Data Harmonization Via Regularized Nonparametric Mixing Distribution Estimation
Steven Wilkins-Reeves, Yen-Chi Chen, Kwun Chuen Gary Chan
Data harmonization is the process by which an equivalence is developed between two variables measuring a common trait. Our problem is motivated by dementia…

Mode and Ridge Estimation in Euclidean and Directional Product Spaces: A Mean Shift Approach
Yikun Zhang, Yen-Chi Chen
The set of local modes and the ridge lines estimated from a dataset are important summary characteristics of the data-generating distribution. In this work, we…

Skeleton Clustering: Dimension-Free Density-based Clustering
Zeyu Wei, Yen-Chi Chen
We introduce a density-based clustering method called skeleton clustering that can detect clusters in multivariate and even high-dimensional data with…

Importance Sampling and its Optimality for Stochastic Simulation Models
Yen-Chi Chen, Youngjun Choe
We consider the problem of estimating an expected outcome from a stochastic simulation model. Our goal is to develop a theoretical framework on importance…

Nonparametric Inference via Bootstrapping the Debiased Estimator
Gang Cheng, Yen-Chi Chen
In this paper, we propose to construct confidence bands by bootstrapping the debiased kernel density estimator (for density estimation) and the debiased local…

Nonparametric Pattern-Mixture Models for Inference with Missing Data
Yen-Chi Chen, Mauricio Sadinle
Pattern-mixture models provide a transparent approach for handling missing data, where the full-data distribution is factorized in a way that explicitly shows…

Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III
Yen-Chi Chen, Shirley Ho, Jonathan Blazek, Siyu He, Rachel Mandelbaum, Peter Melchior, Sukhdeep Singh
Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample…

Measuring Human Activity Spaces from GPS Data with Density Ranking and Summary Curves
Yen-Chi Chen, Adrian Dobra
Activity spaces are fundamental to the assessment of individuals' dynamic exposure to social and environmental risk factors associated with multiple spatial…

Generalized Cluster Trees and Singular Measures
Yen-Chi Chen
In this paper, we study the $\alpha$-cluster tree ($\alpha$-tree) under both singular and nonsingular measures. The $\alpha$-tree uses probability contents…

On the use of bootstrap with variational inference: Theory, interpretation, and a two-sample test example
Yen-Chi Chen, Y. Samuel Wang, Elena A. Erosheva
Variational inference is a general approach for approximating complex density functions, such as those arising in latent variable models, popular in machine…

Functional Summaries of Persistence Diagrams
Eric Berry, Yen-Chi Chen, Jessi Cisewski-Kehe, Brittany Terese Fasy
One of the primary areas of interest in applied algebraic topology is persistent homology, and, more specifically, the persistence diagram. Persistence…

Modal Regression using Kernel Density Estimation: a Review
Yen-Chi Chen
We review recent advances in modal regression studies using kernel density estimation. Modal regression is an alternative approach for investigating…

A Note on Community Trees in Networks
Ruqian Chen, Yen-Chi Chen, Wei Guo, Ashis G. Banerjee
We introduce the concept of community trees that summarizes topological structures within a network. A community tree is a tree structure representing clique…

A Tutorial on Kernel Density Estimation and Recent Advances
Yen-Chi Chen
This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological…

Statistical Inference using the Morse-Smale Complex
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
The Morse-Smale complex of a function $f$ decomposes the sample space into cells where $f$ is increasing or decreasing. When applied to nonparametric density…

Statistical Inference for Cluster Trees
Jisu Kim, Yen-Chi Chen, Sivaraman Balakrishnan, Alessandro Rinaldo, Larry Wasserman
A cluster tree provides a highly-interpretable summary of a density function by representing the hierarchy of its high-density clusters. It is estimated using…

Detecting Effects of Filaments on Galaxy Properties in the Sloan Digital Sky Survey III
Yen-Chi Chen, Shirley Ho, Rachel Mandelbaum, Neta A. Bahcall, Joel R. Brownstein, Peter E. Freeman, Christopher R. Genovese, Donald P. Schneider, Larry Wasserman
We study the effects of filaments on galaxy properties in the Sloan Digital Sky Survey (SDSS) Data Release 12 using filaments from the `Cosmic Web…

Statistical Inference Using Mean Shift Denoising
Yunhua Xiang, Yen-Chi Chen
In this paper, we study how the mean shift algorithm can be used to denoise a dataset. We introduce a new framework to analyze the mean shift algorithm as a…

Density Level Sets: Asymptotics, Inference, and Visualization
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
We derive asymptotic theory for the plug-in estimate for density level sets under Hausdoff loss. Based on the asymptotic theory, we propose two bootstrap…

Nonparametric modal regression
Yen-Chi Chen, Christopher R. Genovese, Ryan J. Tibshirani, Larry Wasserman
Modal regression estimates the local modes of the distribution of $Y$ given $X=x$, instead of the mean, as in the usual regression sense, and can hence reveal…

A Comprehensive Approach to Mode Clustering
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator's modes. We provide…

Asymptotic theory for density ridges
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
The large sample theory of estimators for density modes is well understood. In this paper we consider density ridges, which are a higher-dimensional extension…

Statistical Analysis of Persistence Intensity Functions
Yen-Chi Chen, Daren Wang, Alessandro Rinaldo, Larry Wasserman
Persistence diagrams are two-dimensional plots that summarize the topological features of functions and are an important part of topological data analysis. A…

Cosmic Web Reconstruction through Density Ridges: Catalogue
Yen-Chi Chen, Shirley Ho, Jon Brinkmann, Peter E. Freeman, Christopher R. Genovese, Donald P. Schneider, Larry Wasserman
We construct a catalogue for filaments using a novel approach called SCMS (subspace constrained mean shift; Ozertem & Erdogmus 2011; Chen et al. 2015)…

Cosmic Web Reconstruction through Density Ridges: Method and Algorithm
Yen-Chi Chen, Shirley Ho, Peter E. Freeman, Christopher R. Genovese, Larry Wasserman
The detection and characterization of filamentary structures in the cosmic web allows cosmologists to constrain parameters that dictates the evolution of the…

Investigating Galaxy-Filament Alignments in Hydrodynamic Simulations using Density Ridges
Yen-Chi Chen, Shirley Ho, Ananth Tenneti, Rachel Mandelbaum, Rupert Croft, Tiziana DiMatteo, Peter E. Freeman, Christopher R. Genovese, Larry Wasserman
In this paper, we study the filamentary structures and the galaxy alignment along filaments at redshift $z=0.06$ in the MassiveBlack-II simulation, a state-of…

Optimal Ridge Detection using Coverage Risk
Yen-Chi Chen, Christopher R. Genovese, Shirley Ho, Larry Wasserman
We introduce the concept of coverage risk as an error measure for density ridge estimation. The coverage risk generalizes the mean integrated square error to…

Risk Bounds For Mode Clustering
Martin Azizyan, Yen-Chi Chen, Aarti Singh, Larry Wasserman
Density mode clustering is a nonparametric clustering method. The clusters are the basins of attraction of the modes of a density estimator. We study the risk…

Generalized Mode and Ridge Estimation
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
The generalized density is a product of a density function and a weight function. For example, the average local brightness of an astronomical image is the…

Uncertainty Measures and Limiting Distributions for Filament Estimation
Yen-Chi Chen, Christopher R. Genovese, Larry Wasserman
A filament is a high density, connected region in a point cloud. There are several methods for estimating filaments but these methods do not provide any…