A tutorial on kernel density estimation and recent advances
YC Chen - Biostatistics & Epidemiology, 2017 - Taylor & Francis
This tutorial provides a gentle introduction to kernel density estimation (KDE) and recent
advances regarding confidence bands and geometric/topological features. We begin with a …
advances regarding confidence bands and geometric/topological features. We begin with a …
Manifold learning: What, how, and why
Manifold learning (ML), also known as nonlinear dimension reduction, is a set of methods to
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …
find the low-dimensional structure of data. Dimension reduction for large, high-dimensional …
Topological data analysis
L Wasserman - Annual Review of Statistics and Its Application, 2018 - annualreviews.org
Topological data analysis (TDA) can broadly be described as a collection of data analysis
methods that find structure in data. These methods include clustering, manifold estimation …
methods that find structure in data. These methods include clustering, manifold estimation …
High-dimensional data bootstrap
V Chernozhukov, D Chetverikov… - Annual Review of …, 2023 - annualreviews.org
This article reviews recent progress in high-dimensional bootstrap. We first review high-
dimensional central limit theorems for distributions of sample mean vectors over the …
dimensional central limit theorems for distributions of sample mean vectors over the …
Lasso guarantees for β-mixing heavy-tailed time series
Many theoretical results for lasso require the samples to be iid Recent work has provided
guarantees for lasso assuming that the time series is generated by a sparse Vector …
guarantees for lasso assuming that the time series is generated by a sparse Vector …
Nonparametric modal regression
Nonparametric modal regression Page 1 The Annals of Statistics 2016, Vol. 44, No. 2, 489–514
DOI: 10.1214/15-AOS1373 © Institute of Mathematical Statistics, 2016 NONPARAMETRIC …
DOI: 10.1214/15-AOS1373 © Institute of Mathematical Statistics, 2016 NONPARAMETRIC …
Density level sets: Asymptotics, inference, and visualization
YC Chen, CR Genovese… - Journal of the American …, 2017 - Taylor & Francis
We study the plug-in estimator for density level sets under Hausdorff loss. We derive
asymptotic theory for this estimator, and based on this theory, we develop two bootstrap …
asymptotic theory for this estimator, and based on this theory, we develop two bootstrap …
Detecting effects of filaments on galaxy properties in the Sloan Digital Sky Survey III
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 Reconstruction'catalogue, a …
(SDSS) Data Release 12 using filaments from the 'Cosmic Web Reconstruction'catalogue, a …
Fitting a putative manifold to noisy data
C Fefferman, S Ivanov, Y Kurylev… - … On Learning Theory, 2018 - proceedings.mlr.press
In the present work, we give a solution to the following question from manifold learning.
Suppose data belonging to a high dimensional Euclidean space is drawn independently …
Suppose data belonging to a high dimensional Euclidean space is drawn independently …
Basement fault activation before larger earthquakes in Oklahoma and Kansas
Oklahoma and Kansas experienced unprecedented seismic activity over the past decade
due to earthquakes associated with unconventional hydrocarbon development. The modest …
due to earthquakes associated with unconventional hydrocarbon development. The modest …