Results 11 to 20 of about 665,590 (295)
New Bandwidth Selection for Kernel Quantile Estimators [PDF]
Journal of Probability and Statistics, 2012 We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a ...
Ali Al-Kenani, Keming Yu
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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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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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A new family of kernels from the beta polynomial kernels with applications in density estimation
IJAIN (International Journal of Advances in Intelligent Informatics), 2020 One of the fundamental data analytics tools in statistical estimation is the non-parametric kernel method that involves probability estimates production.
Israel Uzuazor Siloko +2 more
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Spatial Smoothing Effect on Group-Level Functional Connectivity during Resting and Task-Based fMRI
Sensors, 2023 Spatial smoothing is a preprocessing step applied to neuroimaging data to enhance data quality by reducing noise and artifacts. However, selecting an appropriate smoothing kernel size can be challenging as it can lead to undesired alterations in final ...
Cemre Candemir
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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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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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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 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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CoRR, 2020 Image acquisition and segmentation are likely to introduce noise. Further image processing such as image registration and parameterization can introduce additional noise. It is thus imperative to reduce noise measurements and boost signal. In order to increase the signal-to-noise ratio (SNR) and smoothness of data required for the subsequent random ...
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