Results 1 to 10 of about 5,166 (159)
Energy Reports, 2022 In today’s increasingly serious world energy crisis, Renewable energy such as wind energy has gradually penetrated into life. Aiming at the uncertainty of wind power and the need of a mass of sample data in nonparametric kernel density estimation, a wind
Kai Zhang +6 more
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Probit transformation for nonparametric kernel estimation of the copula density [PDF]
Bernoulli, 2017 Copula modelling has become ubiquitous in modern statistics. Here, the problem of nonparametrically estimating a copula density is addressed. Arguably the most popular nonparametric density estimator, the kernel estimator is not suitable for the unit-square-supported copula densities, mainly because it is heavily affected by boundary bias issues.
Gery Geenens, Arthur Charpentier
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Nonparametric Estimation of a Mixing Density via the Kernel Method [PDF]
Journal of the American Statistical Association, 1997 We present a method to estimate the latent distribution for a mixture model. Our method is motivated by the standard kernel density estimation but instead of using an estimate based on the unobserved latent variables, we take the expectation with respect to their distribution conditional on the data. The resulting estimator is continuous and, hence, is
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Energies, 2019 The uncertainty of wind power brings many challenges to the operation and control of power systems, especially for the joint operation of multiple wind farms.
Nan Yang +6 more
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Nonparametric direct density ratio estimation using beta kernel [PDF]
Statistics, 2020 A new nonparametric density ratio estimator using the beta kernel is proposed. It is shown that the beta kernel density ratio estimator (KDRE) is free of boundary or tail bias, and the asymptotic p...
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Nonparametric Kernel Density Estimation Near the Boundary [PDF]
SSRN Electronic Journal, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peter Malec, Melanie Schienle
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Nonparametric density deconvolution by weighted kernel estimators [PDF]
Statistics and Computing, 2008 Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed.
Martin L. Hazelton, Berwin A. Turlach
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Improving for Network Traffic Bayes Classification Method Based on Correlation Information [PDF]
Jisuanji gongcheng, 2016 With the rapid growth of network applications,the efficiency of traditional network traffic classification method based on ports and payloads is reduced greatly.Meanwhile,most traffic flow classification methods do not consider the correlation among the ...
ZHAO Ying,TAN Yang
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Hydrology, 2022 Low-lying coastal communities are often threatened by compound flooding (CF), which can be determined through the joint occurrence of storm surges, rainfall and river discharge, either successively or in close succession.
Shahid Latif, Slobodan P. Simonovic
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Nonparametric localized bandwidth selection for Kernel density estimation [PDF]
Econometric Reviews, 2018 As conventional cross-validation bandwidth selection methods do not work properly in the situation where the data are serially dependent time series, alternative bandwidth selection methods are necessary. In recent years, Bayesian based methods for global bandwidth selection have been studied.
Cheng, Tingting, Gao, Jiti, Zhang, Xibin
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