Results 11 to 20 of about 415,263 (295)
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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Probability density estimation with tunable kernels using orthogonal forward regression [PDF]
, 2009 A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and ...
Chen, S., Harris, Chris J., Hong, Xia
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Kernel density estimation and its application
ITM Web of Conferences, 2018 Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel
Węglarczyk Stanisław
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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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A Deconvolutional Deblurring Algorithm Based on Short- and Long-Exposure Images
Sensors, 2022 An iterative image restoration algorithm, directed at the image deblurring problem and based on the concept of long- and short-exposure deblurring, was proposed under the image deconvolution framework by investigating the imaging principle and existing ...
Yang Bai, Zheng Tan, Qunbo Lv, Min Huang
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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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Research of comparative analysis of nonparametric density estimation by applying Monte Carlo method
Lietuvos Matematikos Rinkinys, 2013 This paper presents nonparametric statistical estimation of distribution density. The Monte Carlo method is used to show the effects of kernel function for multimodal kernel density estimation.
Indrė Drulytė, Tomas Ruzgas
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Time-frequency represetation of radar signals using Doppler-Lag block searching Wigner-Ville distribution [PDF]
, 2018 Radar signals are time-varying signals where the signal parameters change over time. For these signals, Quadratic Time-Frequency Distribution (QTFD) offers advantages over classical spectrum estimation in terms of frequency and time resolution but it ...
Hamdi, Muhammad Noor Muhammad +1 more
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Kernel Quantile Estimators [PDF]
Journal of the American Statistical Association, 1990 Abstract For an estimator of quantiles, the efficiency of the sample quantile can be improved by considering linear combinations of order statistics, that is, L estimators. A variety of such methods have appeared in the literature; an important aspect of this article is that asymptotically several of these are shown to be kernel estimators with a ...
Simon J. Sheather, J. S. Marron
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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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