Prediction-based variable selection for component-wise gradient boosting.

IF 1.2 4区 数学
International Journal of Biostatistics Pub Date : 2023-11-27 eCollection Date: 2024-05-01 DOI:10.1515/ijb-2023-0052
Sophie Potts, Elisabeth Bergherr, Constantin Reinke, Colin Griesbach
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引用次数: 0

Abstract

Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several modifications of the original algorithm have been developed, that mainly focus on different stopping criteria, leaving the actual variable selection mechanism untouched. We investigate different prediction-based mechanisms for the variable selection step in model-based component-wise gradient boosting. These approaches include Akaikes Information Criterion (AIC) as well as a selection rule relying on the component-wise test error computed via cross-validation. We implemented the AIC and cross-validation routines for Generalized Linear Models and evaluated them regarding their variable selection properties and predictive performance. An extensive simulation study revealed improved selection properties whereas the prediction error could be lowered in a real world application with age-standardized COVID-19 incidence rates.

基于预测的梯度增强变量选择。
基于模型的组件梯度增强是一种流行的数据驱动变量选择工具。为了进一步提高其预测和选择质量,对原始算法进行了一些修改,主要关注不同的停止准则,而没有改变实际的变量选择机制。我们研究了基于模型的组件梯度增强中变量选择步骤的不同基于预测的机制。这些方法包括赤池氏信息准则(Akaikes Information Criterion, AIC)以及依赖于通过交叉验证计算的组件测试误差的选择规则。我们实现了广义线性模型的AIC和交叉验证例程,并评估了它们的变量选择特性和预测性能。一项广泛的模拟研究揭示了改进的选择特性,而在年龄标准化的COVID-19发病率的现实世界应用中,预测误差可以降低。
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
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.
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