对于具有半连续结果的回归模型的诊断
1School of Statistics, University of Minnesota, Minneapolis, MN 55455, United States.
Biometrics
|March 12, 2024
概括
研究人员开发了新的残余值来评估半连续数据的回归模型,如医疗保健支出. 这个工具有助于检测模型的错误规范,提高数据分析的准确性.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 生物统计学 生物统计学
背景情况:
- 半连续结果 (例如,医疗保健支出) 在许多领域都是常见的.
- 使用标准回归模型 (Tobit,Tweedie,两部分),但需要对模型的充分性进行检查.
- 现有的诊断工具对于零点质量的数据是不够的.
研究的目的:
- 为具有半连续结果的回归模型提出一种新的残留诊断工具.
- 为了解决标准诊断方法对具有零膨胀结构的数据的局限性.
- 提供一种可靠的方法来评估模型合适性和识别错误规格.
主要方法:
- 为半连续数据开发适用于一般回归模型的新类残留物.
- 理论分析显示在正确规范下均分布,在错误规范下偏差.
- 拟议的残留物应用于样本内模型验证和样本外预测分布评估.
主要成果:
- 当回归模型被正确指定时,拟议的残留物表现出均分布.
- 与均分布的偏差表明模型的错误规格.
- 该方法在分析来自美国医疗支出小组调查的医疗支出数据方面被证明是有效的.
结论:
- 新的余值为验证具有半连续结果的回归模型提供了有价值的工具.
- 这种方法提高了零膨胀数据的领域分析的可靠性.
- 该方法广泛适用于各种回归模型和数据集.
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