Results 21 to 30 of about 4,458,250 (263)
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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Efficient Density Estimation for High-Dimensional Data
IEEE Access, 2022 Multivariate density estimation methods typically work well in low dimensions and their extension to data analytics in high dimensions domain has proven challenging. For density estimation in high-dimensional big data domains, the non-parametric Bayesian
Aref Majdara, Saeid Nooshabadi
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A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research [PDF]
Geoscientific Model Development, 2023 Probability density functions (PDFs) provide information about the probability of a random variable taking on a specific value. In geoscience, data distributions are often expressed by a parametric estimation of their PDF, such as, for example, a ...
M.-T. Pelz +5 more
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OCEANS 2016 MTS/IEEE Monterey, 2016 The estimation of environmental contours of extreme sea states characterized by significant wave height and energy period for the purposes of reliability-based offshore design is a problem that has been tackled in many different ways. Many of the methods used to generate such contours rely on parametric approaches that require an a priori assumption of
Aubrey Eckert-Gallup, Nevin Martin
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Using conditional kernel density estimation for wind power density forecasting [PDF]
, 2012 Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind ...
Jeon, Jooyoung, Taylor, James
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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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Acta Scientiarum Polonorum. Administratio Locorum, 2021 Motives: Using Points-of-Interest (POIs) data and GIS software, the spatial heterogeneity of different types of accommodation could cheap, easily and quick be analyzed.
Mirosław Bełej
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Energies, 2020 The accurate modeling of the charging behaviors for electric vehicles (EVs) is the basis for the charging load modeling, the charging impact on the power grid, orderly charging strategy, and planning of charging facilities.
Lixing Chen, Xueliang Huang, Hong Zhang
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Robotics and Computer-Integrated Manufacturing, 2010 The ability to effectively model dimensional variation of stampings and assemblies is an important tool for manufacturers to investigate, assess and control quality levels of their products. Statistical Process Control (SPC) and Six-Sigma approaches use the assumptions of statistical independence and normally distributed data to create quality process ...
Matuszyk, Timothy +2 more
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Optimal Bandwidth Selection for Kernel Density Functionals Estimation
Journal of Probability and Statistics, 2015 The choice of bandwidth is crucial to the kernel density estimation (KDE) and kernel based regression. Various bandwidth selection methods for KDE and local least square regression have been developed in the past decade.
Su Chen
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