Results 11 to 20 of about 4,458,250 (263)
Transformation kernel density estimation of actuarial loss functions [PDF]
, 2013 [cat] Es presenta un estimador nucli transformat que és adequat per a distribucions de cua pesada. Utilitzant una transformació basada en la distribució de probabilitat Beta l’elecció del paràmetre de finestra és molt directa. Es presenta una aplicació a
Guillén, Montserrat +2 more
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An Improved Variable Kernel Density Estimator Based on L2 Regularization
Mathematics, 2021 The nature of the kernel density estimator (KDE) is to find the underlying probability density function (p.d.f) for a given dataset. The key to training the KDE is to determine the optimal bandwidth or Parzen window.
Yi Jin, Yulin He, Defa Huang
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An Incremental Kernel Density Estimator for Data Stream Computation
Complexity, 2020 Probability density function (p.d.f.) estimation plays a very important role in the field of data mining. Kernel density estimator (KDE) is the mostly used technology to estimate the unknown p.d.f. for the given dataset.
Yulin He +3 more
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Nonparametric distribution of the daylight factor [PDF]
Mathematics in Education, Research and Applications, 2019 Kernel density estimation (KDE) approximates the distribution of statistical data similar to the histogram. The histogram of data is a special kind of the Kernel density.
Dušan Páleš, Milada Balková
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MATEC Web of Conferences, 2022 As hot spot mapping has become an usual technique used worldwide in order to obtain a clearer view on the geographic incidence of several factors from the most diverse fields, this paper aims to present a case study (developed late 2020 and early 2021) regarding air quality status of Craiova – an important Romanian city – involving the most relevant ...
Moldovan Clementina +2 more
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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 ...
Chen, Sheng, Hong, X., Harris, Chris J.
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Fast Kernel Density Estimation with Density Matrices and Random Fourier Features Software
, 2022 Kernel density estimation (KDE) is one of the most widely used nonparametric density estimation methods. The fact that it is a memory-based method, i.e., it uses the entire training data set for prediction, makes it unsuitable for most current big data ...
Osorio, Juan F. +2 more
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Mathematics, 2022 Nonparametric estimation for a probability density function that describes multivariate data has typically been addressed by kernel density estimation (KDE).
Jenny Farmer +2 more
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ISPRS International Journal of Geo-Information, 2019 An urban, commercial central district is often regarded as the heart of a city. Therefore, quantitative research on commercial central districts plays an important role when studying the development and evaluation of urban spatial layouts.
Jing Yang +3 more
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Data in Brief, 2017 The data presented in this article are related to the research article entitled “Convergence of semantics and emotional expression within the IFG pars orbitalis” (Belyk et al., 2017) [1].
Michel Belyk +2 more
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