Open AccessResearch of comparative analysis of nonparametric density estimation by applying Monte Carlo method
Author(s) -
Indrė Drulytė,
Tomas Ruzgas
Publication year - 2013
Publication title -
lietuvos matematikos rinkinys
Language(s) - English
Resource type - Journals
eISSN - 2335-898X
pISSN - 0132-2818
DOI - 10.15388/lmr.b.2013.02
Subject(s) - multivariate kernel density estimation , kernel density estimation , nonparametric statistics , monte carlo method , variable kernel density estimation , kernel (algebra) , mathematics , monte carlo method in statistical physics , statistical physics , density estimation , kernel method , hybrid monte carlo , probability density function , kernel smoother , computer science , statistics , markov chain monte carlo , artificial intelligence , physics , radial basis function kernel , combinatorics , estimator , support vector machine
This paper presents nonparametric statistical estimation of distribution density. The Monte Carlo method is used to show the effects of kernel function for multimodal kernel density estimation. Here it is shown that the novel kernel function is effective for asymmetrical heavy tails distributions.