Results 21 to 30 of about 120,823 (294)

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, Starfield, Richard, Getz, Wayne M   +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
doaj  

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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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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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 Fast-Converging Kernel Density Estimator for Dispersion in Horizontally Homogeneous Meteorological Conditions

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
doaj   +1 more source

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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The stochastic approximation method for the estimation of a multivariate probability density

, 2008
We apply the stochastic approximation method to construct a large class of recursive kernel estimators of a probability density, including the one introduced by Hall and Patil (1994).
Mokkadem, Abdelkader, Pelletier, Mariane, Slaoui, Yousri   +2 more
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The uniformly strong consistency of kernel-type distribution estimator under asymptotically almost negatively associated samples

Statistical Theory and Related Fields
This paper studies the kernel-type distribution estimator based on asymptotically almost negatively associated (AANA, for short) samples. The rate of uniformly strong consistency is established under some mild conditions.
Shipeng Wu, Yi Wu, Wenzhi Yang, Xuejun Wang   +3 more
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