A boosting method to select the random effects in linear mixed models.

IF 1.4 4区数学 Q3 BIOLOGY

Biometrics Pub Date : 2024-01-29 DOI:10.1093/biomtc/ujae010

Michela Battauz, Paolo Vidoni

引用次数: 0

Abstract

This paper proposes a novel likelihood-based boosting method for the selection of the random effects in linear mixed models. The nonconvexity of the objective function to minimize, which is the negative profile log-likelihood, requires the adoption of new solutions. In this respect, our optimization approach also employs the directions of negative curvature besides the usual Newton directions. A simulation study and a real-data application show the good performance of the proposal.

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在线性混合模型中选择随机效应的提升方法。

本文为线性混合模型中随机效应的选择提出了一种新的基于似然的提升方法。要最小化的目标函数（即负轮廓对数似然）的非凸性要求采用新的解决方案。在这方面，除了通常的牛顿方向外，我们的优化方法还采用了负曲率方向。模拟研究和实际数据应用显示了该建议的良好性能。

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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来源期刊

Biometrics 生物-生物学

CiteScore

2.70

自引率

5.30%

发文量

178

审稿时长

4-8 weeks

期刊介绍： The International Biometric Society is an international society promoting the development and application of statistical and mathematical theory and methods in the biosciences, including agriculture, biomedical science and public health, ecology, environmental sciences, forestry, and allied disciplines. The Society welcomes as members statisticians, mathematicians, biological scientists, and others devoted to interdisciplinary efforts in advancing the collection and interpretation of information in the biosciences. The Society sponsors the biennial International Biometric Conference, held in sites throughout the world; through its National Groups and Regions, it also Society sponsors regional and local meetings.