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Kernel density estimation

Known as: Kernel density estimate, Kernel density, Parzen Windows 
In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel… 
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Papers overview

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Highly Cited
2013
Highly Cited
2013
Over the past few decades, many techniques have been developed for the log e valuation of organic-rich rocks (ORR). More recently… 
2013
2013
In the L-estimation and compressive sensing some arbitrarily positioned samples of the signal are either so heavily corrupted by… 
2006
2006
It is known that multicarrier code-division multiple-access (MC-CDMA) systems suffer from multiaccess interference (MAI) when the… 
2005
2005
K Ke er rn ne el l H Ho om me e R Ra an ng ge e E Es st ti im ma at ti io on n f fo or r A Ar rc cG GI IS S, , u us si in ng g V… 
2003
2003
In this paper, several approaches that can be used to improve biometric authentication applications are proposed. The idea is… 
Review
1999
Review
1999
Dealing with data files statisticians often have to consider the problem of missing data due both to unit nonresponse (complete… 
1998
1998
We compare the ability of three exemplar-based memory models, each using three different face stimulus representations, to… 
1995
1995
A simple method for estimating the Volterra kernels of cubic systems with a zero-mean i.i.d. input is presented. This method… 
1992
1992
A general relation between the cumulant functions and the direction of arrival (DOA) parameters is developed. Using this relation… 
1989
1989
SUMMARY The paper makes a critical assessment of Aitchison's criterion of density estimation. It seems not to satisfy certain…