Results 1 to 10 of about 257,461 (188)
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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A Kernel-Based Calculation of Information on a Metric Space
Entropy, 2013 Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space.
Conor J. Houghton, R. Joshua Tobin
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Kernel density estimation of allele frequency including undetected alleles [PDF]
PeerJWhereas undetected species contribute to estimation of species diversity, undetected alleles have not been used to estimated genetic diversity. Although random sampling guarantees unbiased estimation of allele frequency and genetic diversity measures ...
Satoshi Aoki, Keita Fukasawa
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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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Adaptive Kernel Density Estimation [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2003 This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated ...
Van Kerm, Philippe, Van Kerm, Philippe
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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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Robust kernel density estimation [PDF]
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008 We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density estimator (KDE) with ideas from classical $M$-estimation.
Kim, JooSeuk, Scott, Clayton D.
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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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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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