Results 21 to 30 of about 1,617,710 (291)
Multivariate Kernel Smoothing and Its Applications
Journal of the American Statistical Association, 2020 Multivariate Kernel Smoothing and Its Applications, by J.E. Chacón and T. Duong, provides a comprehensive and up-todate introduction of multivariate density estimation.
Qing Wang
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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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Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications
Algorithms, 2022 We propose an alternative maximum entropy approach to learning the spectra of massive graphs. In contrast to state-of-the-art Lanczos algorithm for spectral density estimation and applications thereof, our approach does not require kernel smoothing.
Diego Granziol +5 more
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Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2004 Kernel smoothing provides a simple way for finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model and a random design regression model.
Jitka Poměnková
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Adversarially Robust Kernel Smoothing
, 2021 We propose a scalable robust learning algorithm combining kernel smoothing and robust optimization. Our method is motivated by the convex analysis perspective of distributionally robust optimization based on probability metrics, such as the Wasserstein distance and the maximum mean discrepancy.
Zhu, Jia-Jie +3 more
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Kernel smoothing dos dados de chuva no Nordeste [PDF]
Revista Brasileira de Engenharia Agrícola e Ambiental, 2014 O regime de chuvas sobre o Nordeste do Brasil é bastante complexo, sendo considerado sazonal, além de sofrer fortes influências dos fenômenos El Niño, La Niña e outros sistemas meteorológicos, como o dipolo, atuantes sobre as bacias do oceano Atlântico ...
Nyedja F. M. Barbosa +5 more
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E-Jurnal Matematika, 2018 Nonparametric regression can be applied for some data types one of them is time series data. The technique of this method is called smoothing technique.
DEWA AYU DWI ASTUTI +2 more
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Kernel Density Estimators for Gaussian Mixture Models
Lithuanian Journal of Statistics, 2013 The problem of nonparametric estimation of probability density function is considered. The performance of kernel estimators based on various common kernels and a new kernel K (see (14)) with both fixed and adaptive smoothing bandwidth is compared in ...
Tomas Ruzgas, Indrė Drulytė
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Effects of spatial smoothing on group-level differences in functional brain networks
Network Neuroscience, 2020 Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations.
Ana María Triana +3 more
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Nonparametric estimate remarks
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2006 Kernel smoothers belong to the most popular nonparametric functional estimates. They provide a simple way of finding structure in data. The idea of the kernel smoothing can be applied to a simple fixed design regression model.
Jitka Poměnková
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