Title: Multivariate Density Estimation Using a Multivariate Weighted Log-Normal Kernel

Author(s): Gaku Igarashi
Issue: Volume 80 Series A Part 2 Year 2018
Pages: 247 -- 266
This paper suggests a multivariate asymmetric kernel density estimation using a multivariate weighted log-normal (LN) kernel for non-negative multivariate data. Asymptotic properties of the multivariate weighted LN kernel density estimator are studied. Simulation studies are also conducted in the bivariate situation.
AMS (2000) subject classification. Primary 62G07; Secondary 62G20.
Keywords and phrases: Nonparametric density estimation, Boundary problem, Asymmetric kernel, Multivariate log-normal density.