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Nonparametric Density Estimation Using Copula Transform, Bayesian Sequential Partitioning, and Diffusion-Based Kernel Estimator.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Apr2020, Vol. 32 Issue 4, p821-826. 6p. - Publication Year :
- 2020
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 32
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 143313737
- Full Text :
- https://doi.org/10.1109/TKDE.2019.2930052