Exact Boundary Correction Methods for Multivariate Kernel Density Estimation

This paper develops a method to obtain multivariate kernel functions for density estimation problems in which the function is defined on compact support. If domain-specific knowledge requires certain conditions be satisfied at boundary of support an unknown density, proposed incorporates information contained into estimators. The provides exact that satisfies conditions, even small samples. Existing methods primarily deal with one-sided one-dimensional problem. We consider two-sided interval and extend it multi-dimensional