A Fast Non-parametric Density Estimation Algorithm

Report ID: 
1995-20
Authors: 
Omer Egecioglu and Ashok Srinivasan
Date: 
1995-10-01 05:00:00

Abstract

Non-parametric density estimation is the problem of approximating the values ofa probability density function, given samples from the associateddistribution. Non-parametric estimation finds applications in discriminantanalysis, cluster analysis, and flow calculations based on Smoothed ParticleHydrodynamics. Usual estimators make use of kernel functions, and require onthe order of $n^2$ arithmetic operations to evaluate the density at $n$ samplepoints. We describe a sequence of special weight functions which requiresalmost linear number of operations in $n$ for the same computation.

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