Deconvolving multivariate kernel density estimates from contaminated associated observations

Abstract

We consider the estimation of the multivariate probability density function f(x/sub 1/,...,x/sub p/) of X/sub 1/,...,X/sub p/ of a stationary positively or negatively associated (PA or NA) random process {X/sub i/}/sub i=1//sup /spl infin// from noisy observations. Both ordinary smooth and super smooth noise are considered. Quadratic mean and asymptotic normality results are established.