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不同回归模型的性能评估:应用在乳腺癌患者的数据
Mona Mahmoud Abo El Nasr1, Alaa A Abdelmegaly2, Doaa A Abdo3
1Department of Applied Statistics and Insurance, Faculty of Commerce, Mansoura University, Mansoura, 33516, Egypt. m_eldeep@mans.edu.eg.
Scientific reports
|June 5, 2024
概括
这项研究比较了乳腺癌数据的线性回归模型,发现Beta回归在处理多线性方面表现出色. 回归也表现得很好,证明了这些统计方法的实际应用.
科学领域:
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
- 统计建模 统计建模
背景情况:
- 多对线性在分析复杂的医疗数据集 (如乳腺癌患者数据) 中带来了挑战.
- 线性回归模型被广泛使用,但可能对多线性敏感.
- 评估各种回归技术对于准确的患者数据分析至关重要.
研究的目的:
- 综合分析和比较不同的线性回归模型.
- 专门解决和评估乳腺癌患者数据中的多线性挑战.
- 为了确定这个特定数据集最有效的回归模型.
主要方法:
- 综合增量模型 (GAM) 的比较,Beta回归,GAM Beta,Ridge回归和Beta Ridge回归.
- 使用R软件进行数据分析和模型实现.
- 评估基于两个关键的统计标准:阿卡伊克信息标准 (AIC) 和贝叶斯信息标准 (BIC).
主要成果:
- 贝塔回归模型实现了异常的贝叶斯信息标准 (BIC) -5520.416.
- 斜坡回归模型产生了最好的Akaike信息标准 (AIC) 得分 -8002.647.
- 贝塔回归在管理多对线性问题方面表现出卓越的表现.
结论:
- 贝塔回归模型在解决乳腺癌数据的多线性方面非常有效.
- 斜坡回归也表现出强的表现,其最佳AIC表示.
- 在AIC和BIC之间做出选择,特别是对于通用添加模型 (GAM),取决于捕捉模型的复杂性和灵活性.
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