Open AccessNonparametric Least Squares Mixture Density Estimation
Author(s) -
Chew-Seng Chee
Publication year - 2013
Publication title -
jurnal teknologi/jurnal teknologi
Language(s) - English
Resource type - Journals
eISSN - 2180-3722
pISSN - 0127-9696
DOI - 10.11113/jt.v63.1905
Subject(s) - kernel density estimation , multivariate kernel density estimation , estimator , density estimation , nonparametric statistics , statistics , mathematics , mixture model , variable kernel density estimation , least squares function approximation , mixture distribution , algorithm , computer science , probability density function , kernel method , artificial intelligence , support vector machine
In this paper, we consider using nonparametric mixtures for density estimation. The mixture density estimation problem simply reduces to the problem of estimating a mixing distribution in the nonparametric mixture model. We focus on the least squares method for mixture density estimation problem. In a simulation experiment, the performance of the least squares mixture density estimator (MDE) and the kernel density estimator (KDE) is assessed by the mean integrated squared error. The performance improvement of MDE over KDE for some common densities is achieved by using cross-validation method for bandwidth selection.