Kernel density
Some Improvement on Convergence Rates of Kernel Density Estimator
... two kernel density estimators are introduced and ...ordinary kernel density estimator. The second proposed density estimator is a geometric extrapolation of the first bias reduced ...
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Methods for Shape-Constrained Kernel Density Estimation
... both density estimation and regression; and Du et ...on kernel regression ...shape-constrained kernel density estimation, are discussed further in Section ...
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Kernel density construction using orthogonal forward regression
... constructing kernel density estimates based on a regression approach that directly optimizes generalization ...the density construction is ensured using an orthogonal forward regression, and the ...
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Validating protein structure using kernel density estimates
... Data on the (two-dimensional) torus are commonly found in descriptions of protein structure. Here, the protein backbone is given by a set of atom co-ordinates in R 3 which can then be converted (without any loss of ...
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Kernel density classification and boosting: an L2 sub analysis
... boosting kernel density ...in kernel density estimation and indicate how it will enjoy similar properties for ...boosting kernel classifiers reduces the bias whilst only slightly ...
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A Kernel Density Estimation-Based Approach To Option Pricing
... with kernel density smoothing in order to generate realistic price estimates for various ...unknown density function of a large number of Monte Carlo draws, in order to carry out this estimation and ...
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Performance Evaluation of Various Functions for Kernel Density Estimation
... that kernel density estimation(KDE) method and Gaussian mixture model(GMM) perform about equally best among possible background ...of kernel functions and their bandwidths greatly influence the ...
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Using conditional kernel density estimation for wind power density forecasting
... producing density forecasts for the wind power generated at individual wind ...conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of ...
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Forest Fire Risk Mapping by Kernel Density Estimation
... format. Kernel density estimation is a widely preferred method for con- verting historical fire data into a continuous ...study, kernel density estimate of forest fire events in the Middle ...
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Bandwidth selection for kernel density estimation with length-biased data
... n−consistent. Density estimation for this type of data started in the 80’s when Bhattacharyya and Richardson (1988) defined the first density estimator for length-biased data based on the problem of fibres, ...
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Evaluation of single-kernel density of scab-damaged winter wheat
... between kernel density and the inten- sity of visible symptoms of scab in ...the density of wheat ...(single-kernel) density of control and scabby grain for two varieties (cultivated in ...
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A sparse kernel density estimation algorithm using forward constrained regression
... sparse kernel density estimator is constructed in a for- ward constrained regression ...sparse kernel density estimators with comparable accuracy to that of the classical Parzen window ...
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Kernel Density Estimation of Tropical Cyclone Frequencies in the North Atlantic Basin
... Previous research has identified specific areas of frequent tropical cyclone activity in the North Atlantic ba- sin. This study examines long-term and decadal spatio-temporal patterns of Atlantic tropical cyclone fre- ...
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Adaptive bandwidth kernel density estimation for next generation sequencing data
... adaptive-bandwidth kernel density estimators for the reconstruction and visualization of genomic signals underlying ChIP-Seq data, with several ...different kernel functions, we found that all ...
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Robust Kernel Density Estimation
... and kernel methods has focused primarily on su- pervised learning ...to kernel regression has been studied by various authors (Christmann and Steinwart, 2007; Debruyne et ...for kernel-based ...
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Deconvoluting kernel density estimators
... The first term in (2.6) can be much larger than the variance component of the integrated mean squared error of an ordinary kernel density estimator.. This is the price paid for not measu[r] ...
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Do-Validation for Kernel Density Estimation
... We consider pilot-free MISE optimal estimation and pilot-free MISE near optimal estimation an important area of future research in kernel density bandwidth selec- tion. The key research element of ...
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Kernel Density Estimation for Dynamical Systems
... ergodic, kernel density estimation is not universally consistent under the usual ...the kernel density estimation almost surely does not tend to zero in the L 1 ...
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Kernel density estimation on the torus
... toroidal density kernel estima- tor whose weight functions are based on some well-known circular ...with density f — supported on the multidi- mensional torus, and having an absolutely continuous ...
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Improving bias in kernel density estimation
... R q+1 . Unlike several authors who chose the polynomial subject to some opti- mization criterion (see [Berlinet, 1993, Fan & Hu, 1992, Gasser & Muller, 1979, Lejeune and Sarda, 1992, Wand & Schucany, 1990]) ...
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