Nonparametric density estimation using Copula Transform, Bayesian sequential partitioning and diffusion-based Kernel estimator
Department of Electrical and Computer Engineering
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.
IEEE Transactions on Knowledge and Data Engineering
Nonparametric density estimation using Copula Transform, Bayesian sequential partitioning and diffusion-based Kernel estimator.
IEEE Transactions on Knowledge and Data Engineering.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/434
Copyright 2019 IEEE. Publisher's version of record: https://doi.org/10.1109/TKDE.2019.2930052