在巴基斯坦使用双重目标CART估计层特异性健康结果
Muhammad Hamza1, Shakeel Ahmed1
1National University of Sciences and Technology, Islamabad, Pakistan.
PloS one
|February 29, 2024
概括
这项研究引入了一种新的双目标分类和回归树 (CARTs) 方法,用于改进后分层. 该方法提高了层特异性参数的估计,在效率方面超过现有技术.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 机器学习 机器学习
背景情况:
- 后分层化对于估计分层特异性参数至关重要,当只有被抽样单位才知道分群成员身份时.
- 现有的方法往往侧重于预测非采样值或分类单位,但很少同时解决这两点.
- 准确的分类和预测对于可靠地估计跨领域的健康结果和其他研究变量至关重要.
研究的目的:
- 引入一种新的双目标分类和回归树 (CARTs) 方法来估计层特异性参数.
- 导出拟议的总估计器的理论属性.
- 展示基于CART的方法在现实应用中的实际意义和效率,例如健康结果估计.
主要方法:
- 开发一个双目标分类和回归树 (CARTs) 算法.
- 同时优化非样本单位的分类和预测研究变量.
- 拟议的人口总估计器的属性的理论推导.
主要成果:
- 与现有方法相比,拟议的基于CART的方法显著改善了对分层特定参数的估计.
- 新型估计器在各种参数选择中显示出卓越的相对效率.
- 整体模型,特别是随机森林CART,甚至在没有辅助变量的情况下,也优于其他以树为基础和均质的人口模型.
结论:
- 双重目标的CART方法为分层后和分层特定参数估计提供了强大的和高效的方法.
- 随机森林CART模型为调查统计学家提供了一个强大的工具,提高了复杂人口研究的准确性.
- 这种方法对于估计不同领域的健康结果和其他变量特别有价值.
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