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Kernel density estimation and its application
ITM Web of Conferences, 2018 Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel
Węglarczyk Stanisław
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Probability density estimation from optimally condensed data samples [PDF]
, 2003 The requirement to reduce the computational cost of evaluating a point probability density estimate when employing a Parzen window estimator is a well-known problem.
Chao, H., Girolami, M.
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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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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 +3 more
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Confidence Intervals for Kernel Density Estimation [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2004 This article describes asciker and bsciker, two programs that enrich the possibility for density analysis using Stata. asciker and bsciker compute asymptotic and bootstrap confidence intervals for kernel density estimation, respectively, based on the theory of kernel density confidence intervals estimation developed in Hall (1992b)and Horowitz (2001 ...
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A novel method for analysis of offshore sustainable energy systems
Energy Exploration & Exploitation, 2023 In the present research, we have proposed a new adaptive kernel density estimation method formulated on the theory of linear diffusion processes. By examining the calculation results we have found that in the tail region, our proposed new adaptive kernel
Yingguang Wang
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Kernel Density Estimation for Dynamical Systems
J. Mach. Learn. Res., 2016 sponsorship: The authors are grateful to Professor Laszlo Gyorfi, the reviewers, and the action editor for helpful comments that helped improve the quality and the presentation of this paper. The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007 ...
Hang, Hanyuan +3 more
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Probability density estimation with tunable kernels using orthogonal forward regression [PDF]
, 2010 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 ...
Hong, Xia +3 more
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Consistency of the kernel density estimator: a survey [PDF]
Statistical Papers, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wied, Dominik, Weißbach, Rafael
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Relationship Between Neurologic Symptoms and Signs and FMR1 Genotype in Premutation Carriers
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background and Objectives Fragile X‐associated Tremor/Ataxia Syndrome (FXTAS) is the most severe late‐onset condition caused by a premutation in the FMR1 gene, characterized by expanded CGG triplet repeats of 55–200. Clinical presentations of FXTAS, including gait ataxia, kinetic tremor, cognitive decline, and rare Parkinsonism, are linked to ...
Flora Tassone +8 more
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