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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 Scarrott
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Matematika, 2018 Abstrak. Pada penelitian yang dilakukan oleh Setiawan dkk, menyatakan bahwa metode loss distribution approach dengan pendekatan kernel density estimation mampu menghasilkan nilai economic capital yang lebih efisien sebesar 1,6% - 3,2% dibandingkan dengan
Erwan Setiawan, Ramdhan F Suwarman
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Adaptive Kernel Density Estimation [PDF]
The Stata Journal: Promoting communications on statistics and Stata, 2003 This insert describes the module akdensity. akdensity extends the official kdensity that estimates density functions by the kernel method. The extensions are of two types: akdensity allows the use of an “adaptive kernel” approach with varying, rather than fixed, bandwidths; and akdensity estimates pointwise variability bands around the estimated ...
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Variable Kernel Density Estimation
The Annals of Statistics, 1992 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Terrell, George R., Scott, David W.
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EEG Signal Enhancement Using OWA Filter [PDF]
ITM Web of Conferences, 2021 Biomedical signal monitoring and recording are an integral part of medical diagnosis and treatment control mechanisms. For this, enhanced signals with appropriate peak preservation are required.
Yadav Soham +3 more
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Kernel Density Estimation on the Siegel Space with an Application to Radar Processing
Entropy, 2016 This paper studies probability density estimation on the Siegel space. The Siegel space is a generalization of the hyperbolic space. Its Riemannian metric provides an interesting structure to the Toeplitz block Toeplitz matrices that appear in the ...
Emmanuel Chevallier +3 more
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Kernel distribution density estimation based on cross-validation
Lietuvos Matematikos Rinkinys, 2000 The kernel density estimation procedure is proposed. Parameter selection method based on cross-validation technique is analyzed. The results of investigation by simulation means are discussed.
Mindaugas Kavaliauskas
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Proceedings, 2018 Bootstrap methods are used for bandwidth selection in: (1) nonparametric kernel density estimation with dependent data (smoothed stationary bootstrap and smoothed moving blocks bootstrap), and (2) nonparametric kernel hazard rate estimation (smoothed ...
Inés Barbeito, Ricardo Cao
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Improved parameter estimation of Time Dependent Kernel Density by using Artificial Neural Networks
Journal of Finance and Data Science, 2018 Time Dependent Kernel Density Estimation (TDKDE) used in modelling time-varying phenomenon requires two input parameters known as bandwidth and discount to perform.
Xing Wang +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 ...
Bremnes J. B. +11 more
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