Semi-Parametric Estimation Using Bernstein Polynomial and a Finite Gaussian Mixture Model |
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Authors: | Salima Helali Afif Masmoudi Yousri Slaoui |
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Institution: | 1.Mathematics Laboratory, Angers University, 49100 Angers, France;2.Probability and Statistics Laboratory, Sfax University, Sfax 3029, Tunisia;3.Mathematics and Applications Laboratory, Poitiers University, 86073 Poitiers, France |
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Abstract: | The central focus of this paper is upon the alleviation of the boundary problem when the probability density function has a bounded support. Mixtures of beta densities have led to different methods of density estimation for data assumed to have compact support. Among these methods, we mention Bernstein polynomials which leads to an improvement of edge properties for the density function estimator. In this paper, we set forward a shrinkage method using the Bernstein polynomial and a finite Gaussian mixture model to construct a semi-parametric density estimator, which improves the approximation at the edges. Some asymptotic properties of the proposed approach are investigated, such as its probability convergence and its asymptotic normality. In order to evaluate the performance of the proposed estimator, a simulation study and some real data sets were carried out. |
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Keywords: | asymptotic properties Bernstein polynomial EM algorithm Gaussian mixture model kernel estimator shrinkage estimator |
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