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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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Contingent Kernel Density Estimation
PLoS ONE, 2012 Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias.
Fortmann-Roe, Scott +2 more
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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data [PDF]
Journal of Sciences, Islamic Republic of Iran, 2014 Kernel density estimators are the basic tools for density estimation in non-parametric statistics. The k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the ...
V. Fakoor
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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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Large and moderate deviation principles for nonparametric recursive kernel distribution estimators defined by stochastic approximation method [PDF]
Opuscula Mathematica, 2019 In this paper we prove large and moderate deviations principles for the recursive kernel estimators of a distribution function defined by the stochastic approximation algorithm. We show that the estimator constructed using the stepsize which minimize the
Yousri Slaoui
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Double Kernel Estimation of Sensitivities [PDF]
Journal of Applied Probability, 2009 In this paper we address the general issue of estimating the sensitivity of the expectation of a random variable with respect to a parameter characterizing its evolution. In finance, for example, the sensitivities of the price of a contingent claim are called the Greeks. A new way of estimating the Greeks has recently been introduced in Elie, Fermanian
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A Criterion for the Fuzzy Set Estimation of the Regression Function
Journal of Probability and Statistics, 2012 We propose a criterion to estimate the regression function by means of a nonparametric and fuzzy set estimator of the Nadaraya-Watson type, for independent pairs of data, obtaining a reduction of the integrated mean square error of the fuzzy set ...
Jesús A. Fajardo
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Sparse kernel density estimation technique based on zero-norm constraint [PDF]
, 2010 A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity.
Chen, S, Harris, C J, Hong, Xia
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Kernel Density Estimation in the Study of Star Clusters
Open Astronomy, 2016 The kernel estimator method is used to evaluate the surface and spatial star number density in star clusters. Both density maps and radial density profiles are plotted.
Seleznev Anton F.
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Atmosphere, 2021 Kernel smoothers are often used in Lagrangian particle dispersion simulations to estimate the concentration distribution of tracer gasses, pollutants etc.
Gunther Bijloos, Johan Meyers
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