Results 11 to 20 of about 55,286 (309)
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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Graph Bundling by Kernel Density Estimation [PDF]
Computer Graphics Forum, 2012 AbstractWe present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow ...
Christophe Hurter +2 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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Varying kernel density estimation on [PDF]
Statistics & Probability Letters, 2012 In this article a new nonparametric density estimator based on the sequence of asymmetric kernels is proposed. This method is natural when estimating an unknown density function of a positive random variable. The rates of Mean Squared Error, Mean Integrated Squared Error, and the L1-consistency are investigated.
Robert, Mnatsakanov +1 more
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Research of comparative analysis of nonparametric density estimation by applying Monte Carlo method
Lietuvos Matematikos Rinkinys, 2013 This paper presents nonparametric statistical estimation of distribution density. The Monte Carlo method is used to show the effects of kernel function for multimodal kernel density estimation.
Indrė Drulytė, Tomas Ruzgas
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Journal of Statistical Software, 2018 evmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated
Yang Hu, Carl Scarrott
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Approximate inference of the bandwidth in multivariate kernel density estimation [PDF]
, 2011 Kernel density estimation is a popular and widely used non-parametric method for data-driven density estimation. Its appeal lies in its simplicity and ease of implementation, as well as its strong asymptotic results regarding its convergence to the true ...
Sanguinetti, G. +3 more
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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 ...
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An orthogonal forward regression technique for sparse kernel density estimation [PDF]
, 2008 Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates ...
Harris, C. J. +7 more
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