Nonparametric density estimation using Copula Transform, Bayesian sequential partitioning and diffusion-based Kernel estimator

Authors

Aref Majdara, Michigan Technological UniversityFollow
Saeid Nooshabadi, Michigan Technological UniversityFollow

Document Type

Article

Publication Date

7-23-2019

Department

Department of Electrical and Computer Engineering

Abstract

Non-parametric density estimation methods are more flexible than parametric methods, due to the fact that they do not assume any specific shape or structure for the data. Most non-parametric methods, like Kernel estimation, require tuning of parameters to achieve good data smoothing, a non-trivial task, even in low dimensions. In higher dimensions, sparsity of data in local neighborhoods becomes a challenge even for non-parametric methods. In this paper we use the copula transform and two efficient non-parametric methods to develop a new method for improved non-parametric density estimation in multivariate domain. After separation of marginal and joint densities using copula transform, a diffusion-based kernel estimator is employed to estimate the marginals. Next, Bayesian sequential partitioning (BSP) is used in the joint density estimation.

Publisher's Statement

Copyright 2019 IEEE. Publisher's version of record: https://doi.org/10.1109/TKDE.2019.2930052

Publication Title

IEEE Transactions on Knowledge and Data Engineering

Recommended Citation

Majdara, A., & Nooshabadi, S. (2019). Nonparametric density estimation using Copula Transform, Bayesian sequential partitioning and diffusion-based Kernel estimator. IEEE Transactions on Knowledge and Data Engineering. http://doi.org/10.1109/TKDE.2019.2930052
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/434