Maximum Approximate Bernstein Likelihood Estimation of Densities in a Two-sample Semiparametric Model

2021/02/28 by Zhong Zhen Guan, Guan, Zhong
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Genetic and phenotypic traits in livestock #Methodology (stat.ME) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2103.00648

openalex publication_date 2021/02/28 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28

Abstract

Maximum likelihood estimators are proposed for the parameters and the densities in a semiparametric density ratio model in which the nonparametric baseline density is approximated by the Bernstein polynomial model. The EM algorithm is used to obtain the maximum approximate Bernstein likelihood estimates. Simulation study shows that the performance of the proposed method is much better than the existing ones. The proposed method is illustrated by real data examples. Some asymptotic results are also presented and proved.

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