两样本半参数模型的最大近似Bernstein似然估计

IF 0.8 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Nonparametric Statistics Pub Date : 2022-12-21 DOI:10.1080/10485252.2022.2158332

Zhong Guan

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引用次数: 0

摘要

提出了半参数密度比模型中参数和底层密度的极大似然估计，其中非参数基线密度由Bernstein多项式模型近似。EM算法用于获得最大近似Bernstein似然估计。通过两个医学研究的实际数据进行了验证，仿真结果表明该方法比现有方法具有更好的性能。给出并证明了一些渐近结果。

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Maximum approximate Bernstein likelihood estimation in a two-sample semiparametric model

Maximum likelihood estimators are proposed for the parameters and the underlying 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. The proposed method is illustrated by two real data from medical research and is shown by simulation to have better performance than the existing ones. Some asymptotic results are also presented and proved.

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来源期刊

Journal of Nonparametric Statistics 数学-统计学与概率论

CiteScore

1.50

自引率

8.30%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Nonparametric Statistics provides a medium for the publication of research and survey work in nonparametric statistics and related areas. The scope includes, but is not limited to the following topics: Nonparametric modeling, Nonparametric function estimation, Rank and other robust and distribution-free procedures, Resampling methods, Lack-of-fit testing, Multivariate analysis, Inference with high-dimensional data, Dimension reduction and variable selection, Methods for errors in variables, missing, censored, and other incomplete data structures, Inference of stochastic processes, Sample surveys, Time series analysis, Longitudinal and functional data analysis, Nonparametric Bayes methods and decision procedures, Semiparametric models and procedures, Statistical methods for imaging and tomography, Statistical inverse problems, Financial statistics and econometrics, Bioinformatics and comparative genomics, Statistical algorithms and machine learning. Both the theory and applications of nonparametric statistics are covered in the journal. Research applying nonparametric methods to medicine, engineering, technology, science and humanities is welcomed, provided the novelty and quality level are of the highest order. Authors are encouraged to submit supplementary technical arguments, computer code, data analysed in the paper or any additional information for online publication along with the published paper.