A Tutorial on Kernel Density Estimation and Recent Advances
Yen-Chi Chen
- Vol. 1, Iss: 1, pp 161-187
TLDR
In this article, a tutorial provides a gentle introduction to kernel density estimation (KDE) and recent advances regarding confidence bands and geometric/topological features, and a discussion of basi...Abstract:
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 discussion of basi...read more
Citations
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Journal ArticleDOI
Unsupervised Learning Methods for Molecular Simulation Data.
Aldo Glielmo,Brooke E. Husic,Alex Rodriguez,Cecilia Clementi,Frank Noé,Frank Noé,Alessandro Laio,Alessandro Laio +7 more
TL;DR: This Review provides a comprehensive overview of the methods of unsupervised learning that have been most commonly used to investigate simulation data and indicates likely directions for further developments in the field.
Journal ArticleDOI
Numerical phase reduction beyond the first order approximation.
TL;DR: In this paper, the phase dynamics of self-sustained oscillators are reconstructed using a simple algorithm for computation of the phase of a perturbed system, and the equation for the evolution of phase is constructed numerically.
Journal ArticleDOI
Reference evapotranspiration time series forecasting with ensemble of convolutional neural networks
Patrícia de Oliveira e Lucas,Marcos Antonio Alves,Petrônio Cândido de Lima e Silva,Frederico Gadelha Guimarães +3 more
TL;DR: The results showed the feasibility of the CNN models for forecasting ET o and that ensemble models were better than the well-known Seasonal ARIMA and Seasonal Naive and improved predictions in terms of variance, precision and computational cost in relation to the individual CNN models.
Journal ArticleDOI
Real-Time Event Classification in Power System With Renewables Using Kernel Density Estimation and Deep Neural Network
TL;DR: A kernel density estimation approach for accurate real-time classification of events in a power system with renewables using synchrophasor data using a diffusion type kernel density estimator (DKDE) to characterize the shape of 3-D voltage and frequency distribution along time in terms of probability density functions (PDFs).
Book ChapterDOI
A Probability Estimation of Aircraft Departures and Arrivals Delays
Ivan Ostroumov,Nataliia Kuzmenko,Olga A. Sushchenko,Maksym Zaliskyi,Oleksandr Solomentsev,Yuliya Averyanova,S. S. Zhyla,Vladimir Pavlikov,Eduard Tserne,Valerii Konstantinovich Volosyuk,Kostiantyn Dergachov,Olena Havrylenko,Oleksandr Shmatko,Anatoliy Popov,Nikolay Ruzhentsev,Borys Kuznetsov,Tatyana Nikitina +16 more
TL;DR: In this article, the authors proposed to use a trajectory of airplanes to estimate time of airplane delay using Automatic Dependent Surveillance-Broadcast receiver, where a network of software defined radios is used to receive position reports of particular airspace user transmitted by airplane transponder of Mode 1090ES.
References
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BookDOI
Density estimation for statistics and data analysis
TL;DR: The Kernel Method for Multivariate Data: Three Important Methods and Density Estimation in Action.
Journal ArticleDOI
Bootstrap Methods: Another Look at the Jackknife
TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Journal ArticleDOI
Mean shift: a robust approach toward feature space analysis
Dorin Comaniciu,Peter Meer +1 more
TL;DR: It is proved the convergence of a recursive mean shift procedure to the nearest stationary point of the underlying density function and, thus, its utility in detecting the modes of the density.
Journal ArticleDOI
On Estimation of a Probability Density Function and Mode
TL;DR: In this paper, the problem of the estimation of a probability density function and of determining the mode of the probability function is discussed. Only estimates which are consistent and asymptotically normal are constructed.
Journal ArticleDOI
Mean shift, mode seeking, and clustering
TL;DR: Mean shift, a simple interactive procedure that shifts each data point to the average of data points in its neighborhood is generalized and analyzed and makes some k-means like clustering algorithms its special cases.