Nonparametric Density Estimation Using Copula Transform, Bayesian Sequential Partitioning, and Diffusion-Based Kernel Estimator | IEEE Journals & Magazine | IEEE Xplore

Nonparametric Density Estimation Using Copula Transform, Bayesian Sequential Partitioning, and Diffusion-Based Kernel Estimator


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....Show More

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.
Published in: IEEE Transactions on Knowledge and Data Engineering ( Volume: 32, Issue: 4, 01 April 2020)
Page(s): 821 - 826
Date of Publication: 23 July 2019

ISSN Information:


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