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Journal of Statistical Software, 2018 evmix is an R package (R Core Team 2017) with two interlinked toolsets: i) for extreme value modeling and ii) kernel density estimation. A key issue in univariate extreme value modeling is the choice of threshold beyond which the asymptotically motivated
Yang Hu, Carl John Scarrott
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Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
Applied Sciences, 2022 Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images.
Shanshan Liu +2 more
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Multivariate kernel density estimation with a parametric support [PDF]
Opuscula Mathematica, 2009 We consider kernel density estimation in the multivariate case, focusing on the use of some elements of parametric estimation. We present a two-step method, based on a modification of the EM algorithm and the generalized kernel density estimator, and ...
Jolanta Jarnicka
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Curve fitting of the corporate recovery rates: the comparison of Beta distribution estimation and kernel density estimation. [PDF]
PLoS ONE, 2013 Recovery rate is essential to the estimation of the portfolio's loss and economic capital. Neglecting the randomness of the distribution of recovery rate may underestimate the risk.
Rongda Chen, Ze Wang
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Pixel-Level Kernel Estimation for Blind Super-Resolution
IEEE Access, 2021 Throughout the past several years, deep learning-based models have achieved success in super-resolution (SR). The majority of these works assume that low-resolution (LR) images are ‘uniformly’ degraded from their corresponding high ...
Jaihyun Lew, Euiyeon Kim, Jae-Pil Heo
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Contingent kernel density estimation. [PDF]
PLoS ONE, 2012 Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points.
Scott Fortmann-Roe +2 more
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Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution
Sensors, 2023 Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways
Jongeun Park, Hansol Kim, Moon Gi Kang
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Predictive accuracy of option pricing models considering high-frequency data
Ekonomski Vjesnik, 2021 Purpose: Recently, considerable attention has been given to forecasting, not only the mean and the variance, but also the entire probability density function (pdf) of the underlying asset.
Josip Arnerić, Maria Čuljak
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A New Kernel Estimator of Copulas Based on Beta Quantile Transformations
Mathematics, 2021 A copula is a multivariate cumulative distribution function with marginal distributions Uniform(0,1). For this reason, a classical kernel estimator does not work and this estimator needs to be corrected at boundaries, which increases the difficulty of ...
Catalina Bolancé, Carlos Alberto Acuña
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Mars Image Super-Resolution Based on Generative Adversarial Network
IEEE Access, 2021 High-resolution (HR) Mars images have great significance for studying the land-form features of Mars and analyzing the climate on Mars. Nowadays, the mainstream image super-resolution methods are based on deep learning or CNNs, which are better than ...
Cong Wang +4 more
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