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kernel density estimation

Robust Kernel Density Estimation

Robust Kernel Density Estimation

... robust 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 ...

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Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

... This paper presents MCMC algorithms to estimate the optimal bandwidth for multivariate kernel density estimation via the likelihood cross-validation criterion. This represents the first data-driven ...

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Methods for Shape-Constrained Kernel Density Estimation

Methods for Shape-Constrained Kernel Density Estimation

... a density to have particular characteristics like monotonicity or ...in kernel density estimation (hopefully reducing sensitivity to bandwidth choice in the ...

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Kernel Density Estimation of Tropical Cyclone Frequencies in the North Atlantic Basin

Kernel Density Estimation of Tropical Cyclone Frequencies in the North Atlantic Basin

... [3], kernel density estimation (KDE) identified areas of highest density of occurrence for each category of tropical cyclone in the north Atlantic basin from 1944 through ...hurricane ...

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Bandwidth selection for kernel density estimation with length-biased data

Bandwidth selection for kernel density estimation with length-biased data

... The asymptotic expression of the optimal bootstrap bandwidth can be considered to derive a consistent bandwidth estimate. Cao (1993) suggested such approach for kernel density estimation with ...

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New Gradient Methods for Bandwidth Selection in Bivariate Kernel Density Estimation

New Gradient Methods for Bandwidth Selection in Bivariate Kernel Density Estimation

... bivariate kernel density estimator is fundamental in data smoothing methods especially for data exploration and visualization purposes due to its ease of graphical interpretation of ...bivariate ...

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A Bayesian approach to parameter estimation for kernel density estimation via transformations

A Bayesian approach to parameter estimation for kernel density estimation via transformations

... the kernel density estimation of bivariate insurance claim data via ...the kernel density estimator based on original data does not perform ...the density of the original data ...

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Forest Fire Risk Mapping by Kernel Density Estimation

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 ...

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Performance Evaluation of Various Functions for Kernel Density Estimation

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 ...

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Bandwidth selection for kernel density estimation: a review of fully automatic selectors

Bandwidth selection for kernel density estimation: a review of fully automatic selectors

... Abstract On the one hand, kernel density estimation has become a common tool for empirical studies in any research area. This goes hand in hand with the fact that this kind of estimator is now ...

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A Kernel Density Estimation-Based Approach To Option Pricing

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 ...

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Bayesian classifiers based on kernel density estimation: Flexible classifiers

Bayesian classifiers based on kernel density estimation: Flexible classifiers

... true density of the variables using a parametric ...the estimation of the param- eters is more reliable because they are learned from the partitions induced only by the class (by average n=r cases, where n ...

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Kernel Density Estimation of Reliability With Applications to Extreme Value Distribution

Kernel Density Estimation of Reliability With Applications to Extreme Value Distribution

... non-parametric kernel density estimation and its application to an extreme value distribution, namely the Gumbel probability ...non-parametric kernel density methodology, in order to ...

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Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

Bandwidth Selection for Multivariate Kernel Density Estimation Using MCMC

... This paper presents MCMC sampling algorithms to estimate the optimal bandwidth for mul- tivariate kernel density estimation via the likelihood cross-validation criterion. This represents the first ...

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Approximate inference of the bandwidth in multivariate kernel density estimation

Approximate inference of the bandwidth in multivariate kernel density estimation

... Kernel density estimation is a popular and widely used non-parametric method for data-driven density ...Keywords: kernel density estimation, Bayesian inference, ...

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Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

Nonlinear process fault detection and identification using kernel PCA and kernel density estimation

... bility density functions (PDFs) estimated directly from the T 2 and the Q statistics via a non-parametric tech- nique such as kernel density estimation ...

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Kernel Density Estimation for Dynamical Systems

Kernel Density Estimation for Dynamical Systems

... eter estimation, invariant measure estimation, forecasting, noise detection, among ...the density estimation problem for dynamical systems via a classical nonparametric approach, ...i.e., ...

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Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation

Convergence rates for unconstrained bandwidth matrix selectors in multivariate kernel density estimation

... The purpose of this paper is to derive some results that are helpful in the asymptotic analysis of full (i.e. unconstrained) bandwidth matrix selectors in multivariate kernel density estimation. Our ...

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Prediction of protein secondary structures with a novel kernel density estimation based classifier

Prediction of protein secondary structures with a novel kernel density estimation based classifier

... As mentioned above, the design of Prote2S is based on a novel kernel density estimation algorithm. The mathe- matical fundamentals of the so-called RVKDE can be found in our recent publication [12]. ...

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Anomaly detection using local kernel density estimation and context based regression

Anomaly detection using local kernel density estimation and context based regression

... neighborhood density of Anomaly B is high, it is easily detected by the local density estimation-based ...Gaussian kernel to estimate local densities of ...a density-based anomaly ...

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