Bernstein Polynomial Model for Nonparametric Multivariate Density

Tao Wang; Zhong Guan

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arXiv · 1901.07515

Bernstein Polynomial Model for Nonparametric Multivariate Density

Tao Wang·Zhong Guan

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Abstract

In this paper, we study the Bernstein polynomial model for estimating the multivariate distribution functions and densities with bounded support. As a mixture model of multivariate beta distributions, the maximum (approximate) likelihood estimate can be obtained using EM algorithm. A change-point method of choosing optimal degrees of the proposed Bernstein polynomial model is presented. Under some conditions the optimal rate of convergence in the mean $\chi^2$-divergence of new density estimator is shown to be nearly parametric. The method is illustrated by an application to a real data set. Finite sample performance of the proposed method is also investigated by simulation study and is shown to be much better than the kernel density estimate but close to the parametric ones.

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Tao Wang, Zhong Guan. 2019-01-22. Bernstein Polynomial Model for Nonparametric Multivariate Density. https://arxiv.org/abs/1901.07515

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