Results 31 to 40 of about 46,105 (114)
Improving Radio Source Count Estimation Using Kernel Density Estimation
The Astrophysical JournalRadio source counts provide a fundamental census of cosmic radio emission, yet their estimation is usually based on coarse histograms that suffer from bin-choice bias, boundary effects, and survey incompleteness.
Luozhenhan Liu +3 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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Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
Econometrics, 2016 This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density.
Xibin Zhang +2 more
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A Kernel-Based Calculation of Information on a Metric Space
Entropy, 2013 Kernel density estimation is a technique for approximating probability distributions. Here, it is applied to the calculation of mutual information on a metric space.
Conor J. Houghton, R. Joshua Tobin
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A Novel Flexible Kernel Density Estimator for Multimodal Probability Density Functions
CAAI Transactions on Intelligence TechnologyEstimating probability density functions (PDFs) is critical in data analysis, particularly for complex multimodal distributions. traditional kernel density estimator (KDE) methods often face challenges in accurately capturing multimodal structures due to
Jia‐Qi Chen +5 more
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Acta Universitatis Lodziensis. Folia Oeconomica, 2017 Ad hoc methods in the choice of smoothing parameter in kernel density estimation, although often used in practice due to their simplicity and hence the calculated efficiency, are characterized by quite big error.
Aleksandra Katarzyna Baszczyńska
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Heuristic Kernel Density Estimator for Modal‑Proximity Data
Shuju Caiji Yu ChuliDifferent from the classical probability density estimator construction strategies based on the Parzen window method， we propose a heuristic kernel density estimator （HKDE） based on nearest neighbor error measurement function， to improve the accuracy of ...
HE Yulin +4 more
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Enhancing Broiler Weight Estimation through Gaussian Kernel Density Estimation Modeling
AgricultureThe management of individual weights in broiler farming is not only crucial for increasing farm income but also directly linked to the revenue growth of integrated broiler companies, necessitating prompt resolution.
Yumi Oh +4 more
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MULTILANE TRAFFIC DENSITY ESTIMATION AND TRACKING
Electrica, 2017 As the number of vehicles in roads increases, information of traffic density becomes crucial to municipalities for making better decisions about road management and to the environment for reduced carbon emission.
Mikail YILAN, Mehmet Kemal ÖZDEMİR
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Manifold learning based on kernel density estimation
Учёные записки Казанского университета: Серия Физико-математические науки, 2018 The problem of unknown high-dimensional density estimation has been considered. It has been suggested that the support of its measure is a low-dimensional data manifold. This problem arises in many data mining tasks.
A.P. Kuleshov +2 more
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