Regularization-Based Bootstrap Ranking Model: Identifying Healthcare Indicators Among All Level Income Economies

E. Thompson, Ahmad M. Talafha
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Abstract

This study considers the problem of uncertainty of concurrent variables selection among a potential set of healthcare expenditure predictors. It evaluates two regularization (shrinkage) methods: Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ENET). To improve the accuracy of identifying important and relevant predictors of healthcare cost, the present study proposes a new methodology in the form of a bootstrapped-regularized regression with percentile rankings. A simulation study under various scenarios was implemented to learn the performance of the proposed methodology. The proposed methodology was applied to healthcare expenditure data for all level income economies: lower-income, lower-middle-income, upper-middle-income, and high-income.
基于正则化的Bootstrap排名模型:在所有收入水平的经济体中识别医疗保健指标
本研究考虑了在一组潜在的医疗支出预测因子中并发变量选择的不确定性问题。评估了最小绝对收缩和选择算子(LASSO)和弹性网(ENET)两种正则化(收缩)方法。为了提高识别医疗保健成本重要和相关预测因子的准确性,本研究提出了一种新的方法,即带百分位数排名的自举正则化回归。在不同情况下进行了仿真研究,以了解所提出方法的性能。所提出的方法适用于所有收入水平经济体的医疗保健支出数据:低收入、中低收入、中高收入和高收入。
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