Results 251 to 260 of about 1,722,167 (288)

Generalized Kernel Density Estimator

Theory of Probability & Its Applications, 2000
Summary: We introduce a new class of nonparametric density estimators. It includes the classical kernel density estimators as well as the popular Abramson's estimator. We show that the generalized estimators may perform much better than the classical one if the distribution has a heavy tail.
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SUBSAMPLING FOR DENSITY ESTIMATION

Statistics & Risk Modeling, 2002
Summary: We consider nonparametric density estimation from the point of view of coverage probability. To take into account the problem of bias in bootstrapping nonparametric density kernel estimators, \textit{P. Hall} [Statistics 22, No. 2, 215-232 (1991; Zbl 0809.62031); Ann. Stat. 20, No. 2, 675-694 (1992; Zbl 0748.62028)] showed that it is better to
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Density Estimation.

The Journal of the Operational Research Society, 1986
Chris Beaumont, B. W. Silverman
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Bayesian density estimation via dirichlet density processes

Journal of Nonparametric Statistics, 1996
For the purpose of nonparametric density estimation, a prior distribution is constructed on the space of stepwise constant density functions, not necessarily of bounded support. In particular, the sequence of heights is conditionally distributed a priorias a Dirichlet process on the integers, given a bidimensional mixing parameter.
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VARIABLE KERNEL DENSITY ESTIMATES AND VARIABLE KERNEL DENSITY ESTIMATES

Australian Journal of Statistics, 1990
SummaryThe term “variable kernel density estimate” is sometimes used to mean a kernel density estimate employing a different bandwidth for each data point, and sometimes to denote a kernel density estimate with bandwidth a function of estimation location.
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Electroceramics for High-Energy Density Capacitors: Current Status and Future Perspectives

Chemical Reviews, 2021
ge wang, Zhilun Lu, Linhao Li
exaly  

Density Estimation

1989
Lázió Györfi, Wolfgang Härdle, Pascal Sarda, Philippe Vieu   +3 more
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High power and energy density dynamic phase change materials using pressure-enhanced close contact melting

Nature Energy, 2022
Wuchen Fu, Xiao Yan, Yashraj Gurumukhi
exaly  

Density Estimation

2019
Silvelyn Zwanzig, Behrang Mahjani
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Pareto Density Estimation: A Density Estimation for Knowledge Discovery

2005
Pareto Density Estimation (PDE) as defined in this work is a method for the estimation of probability density functions using hyperspheres. The radius of the hyperspheres is derived from optimizing information while minimizing set size. It is shown, that PDE is a very good estimate for data containing clusters of Gaussian structure. The behavior of the
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