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Contingent kernel density estimation. [PDF]
PLoS ONE, 2012 Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points.
Scott Fortmann-Roe +2 more
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Functional Kernel Density Estimation: Point and Fourier Approaches to Time Series Anomaly Detection [PDF]
Entropy, 2020 We present an unsupervised method to detect anomalous time series among a collection of time series. To do so, we extend traditional Kernel Density Estimation for estimating probability distributions in Euclidean space to Hilbert spaces.
Michael R. Lindstrom +2 more
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Kernel Density Estimation-based Lightweight IoT Anomaly Traffic Detection Method [PDF]
Jisuanji kexue, 2021 In order to effectively deal with the security threats of home and personal Internet of Things(IoT) bot nets,especially for the objective problem of insufficient resources for anomaly detection in the home environment,a kernel density estimation-based ...
ZHANG Ye, LI Zhi-hua, WANG Chang-jie
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Multivariate kernel density estimation with a parametric support [PDF]
Opuscula Mathematica, 2009 We consider kernel density estimation in the multivariate case, focusing on the use of some elements of parametric estimation. We present a two-step method, based on a modification of the EM algorithm and the generalized kernel density estimator, and ...
Jolanta Jarnicka
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Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation. [PDF]
PLoS ONE, 2013 Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk.
Rongda Chen, Ze Wang
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DEMANDE: Density Matrix Neural Density Estimation
IEEE Access, 2023 Density estimation is a fundamental task in statistics and machine learning that aims to estimate, from a set of samples, the probability density function of the distribution that generated them.
Joseph A. Gallego-Mejia +1 more
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An Improved Model for Kernel Density Estimation Based on Quadtree and Quasi-Interpolation
Mathematics, 2022 There are three main problems for classical kernel density estimation in its application: boundary problem, over-smoothing problem of high (low)-density region and low-efficiency problem of large samples.
Jiecheng Wang, Yantong Liu, Jincai Chang
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IEEE Access, 2020 Because general statistics tolerance is not applicable to the induction of non-Gaussian vibration data and the methods for converting non-Gaussian data into Gaussian data are not always effective and can increase the estimation error, a novel kernel ...
Peng Wang +4 more
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Kernel Density Derivative Estimation of Euler Solutions
Applied Sciences, 2023 Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor Euler deconvolution applies additional constraints to the Euler solution using all gravity vectors and the full gravity ...
Shujin Cao +7 more
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Entropy, 2008 At the heart of many ICA techniques is a nonparametric estimate of an information measure, usually via nonparametric density estimation, for example, kernel density estimation.
Julian Sorensen
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