Combining local and global smoothing in multivariate density estimation

TL;DR

This paper introduces a novel non-parametric multivariate density estimation method that combines local and global smoothing techniques, demonstrating promising simulation results and practical application in density-based clustering.

Contribution

The paper proposes a new approach that integrates local and global smoothing for multivariate density estimation without rigid assumptions, enhancing flexibility and effectiveness.

Findings

Encouraging simulation results show improved density estimation.

Application to clustering demonstrates practical utility.

Method offers a flexible alternative to traditional approaches.

Abstract

Non-parametric estimation of a multivariate density estimation is tackled via a method which combines traditional local smoothing with a form of global smoothing but without imposing a rigid structure. Simulation work delivers encouraging indications on the effectiveness of the method. An application to density-based clustering illustrates a possible usage.