Kernel density estimation by stagewise algorithm with a simple dictionary

This study proposes multivariate kernel density estimation by stagewise minimization algorithm based on U-divergence and a simple dictionary. The dictionary consists of an appropriate scalar bandwidth matrix part the original data. resulting estimator brings us data-adaptive weighting parameters matrices, provides sparse representation estimation. We develop non-asymptotic error bound that we obtained via proposed algorithm. It is confirmed from simulation studies performs as well as, or sometimes better than, other well-known estimators.