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Particle size distribution in natural water via density estimation
Author(s) -
Jarnicka Jolanta
Publication year - 2010
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.1079
Subject(s) - multivariate kernel density estimation , kernel density estimation , density estimation , estimator , variable kernel density estimation , mathematics , kernel (algebra) , parametric statistics , statistics , mathematical optimization , biological system , kernel method , computer science , artificial intelligence , support vector machine , combinatorics , biology
In this paper we use density estimation to investigate the particle size distribution in surface water and to extract the components of density functions obtained. This distribution helps to investigate quality of water with respect to its physical characterization. The analysis is conducted using the two‐step density estimation method. The first step of the method is based on parametric density estimation and uses a modification of the EM algorithm to a normal mixture model. In the second step we apply the generalized kernel density estimator, being the convex combination of the well‐known kernel density estimators.