Results 11 to 20 of about 260,346 (290)
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.
JooSeuk Kim, Clayton D. Scott
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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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Kernel density classification and boosting: an L2 sub analysis [PDF]
, 2005 Kernel density estimation is a commonly used approach to classification. However, most of the theoretical results for kernel methods apply to estimation per se and not necessarily to classification.
B.W. Silverman +25 more
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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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Kernel density estimation via diffusion [PDF]
, 2010 We present a new adaptive kernel density estimator based on linear diffusion processes. The proposed estimator builds on existing ideas for adaptive smoothing by incorporating information from a pilot density estimate.
Botev, Z. I. +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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Probability density estimation with tunable kernels using orthogonal forward regression [PDF]
, 2009 A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and ...
Chen, S., Harris, Chris J., Hong, Xia
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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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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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