Choice of degree of Bernstein polynomial model

Tao Wang and Zhong Guan

Statistics & Probability Letters, 2023, vol. 200, issue C

Abstract: Methods based on moments and modes for choosing Bernstein polynomial model degree are proposed. Simulation showed that the mean integrated square errors of the maximum approximate Bernstein likelihood estimates of density using degrees selected by the proposed methods and the change-point method are smaller than that of the kernel density but either closer to or smaller than that of the parametric maximum likelihood estimator.

Keywords: Bernstein polynomial model; Density estimation; Maximum likelihood method (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.spl.2023.109868

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