收缩估计应用于半非参数回归模型。

IF 1.2 4区数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY

International Journal of Biostatistics Pub Date : 2020-08-10 DOI:10.1515/ijb-2018-0109

Hossein Zareamoghaddam, Syed E Ahmed, Serge B Provost

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引用次数: 0

摘要

stein型收缩技术应用于(Ma等人，2015:285-303)最近提出的半非参数回归模型的参数成分。在不确定的先验信息(限制)的基础上，收缩技术被证明可以提高模型的准确性。仿真研究证实了所提估计器的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Shrinkage estimation applied to a semi-nonparametric regression model.

Stein-type shrinkage techniques are applied to the parametric components of a semi-nonparametric regression model recently proposed by (Ma et al. 2015: 285-303). On the basis of an uncertain prior information (restrictions) about the parameters of interest, shrinkage techniques are shown to improve the accuracy of the model. The effectiveness of the proposed estimators are corroborated by a simulation study.

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来源期刊

International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY

CiteScore

2.10

自引率

8.30%

发文量

审稿时长

>12 weeks

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.