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 …
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 …
[图书][B] Multivariate kernel smoothing and its applications
Kernel smoothing has greatly evolved since its inception to become an essential
methodology in the data science tool kit for the 21st century. Its widespread adoption is due …
methodology in the data science tool kit for the 21st century. Its widespread adoption is due …
Rates of estimation of optimal transport maps using plug-in estimators via barycentric projections
Optimal transport maps between two probability distributions $\mu $ and $\nu $ on $\R^ d $
have found extensive applications in both machine learning and statistics. In practice, these …
have found extensive applications in both machine learning and statistics. In practice, these …
Functional data analysis of amplitude and phase variation
The abundance of functional observations in scientific endeavors has led to a significant
development in tools for functional data analysis (FDA). This kind of data comes with several …
development in tools for functional data analysis (FDA). This kind of data comes with several …
Hashing-based-estimators for kernel density in high dimensions
M Charikar, P Siminelakis - 2017 IEEE 58th Annual Symposium …, 2017 - ieeexplore.ieee.org
Given a set of points P⊂ ℝ d and a kernel k, the Kernel Density Estimate at a point x∈ ℝ d is
defined as KDE P (x)= 1/| P| Σy∈ P k (x, y). We study the problem of designing a data …
defined as KDE P (x)= 1/| P| Σy∈ P k (x, y). We study the problem of designing a data …
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 …
Uniform convergence rates for kernel density estimation
H Jiang - International Conference on Machine Learning, 2017 - proceedings.mlr.press
Kernel density estimation (KDE) is a popular nonparametric density estimation method. We
(1) derive finite-sample high-probability density estimation bounds for multivariate KDE …
(1) derive finite-sample high-probability density estimation bounds for multivariate KDE …
Nonparametric ridge estimation
CR Genovese, M Perone-Pacifico, I Verdinelli… - 2014 - projecteuclid.org
We study the problem of estimating the ridges of a density function. Ridge estimation is an
extension of mode finding and is useful for understanding the structure of a density. It can …
extension of mode finding and is useful for understanding the structure of a density. It can …
Near-optimal coresets of kernel density estimates
JM Phillips, WM Tai - Discrete & Computational Geometry, 2020 - Springer
We construct near-optimal coresets for kernel density estimates for points in R^ d R d when
the kernel is positive definite. Specifically we provide a polynomial time construction for a …
the kernel is positive definite. Specifically we provide a polynomial time construction for a …