多项逻辑因子回归对多源功能区块智能缺失数据的回归
Xiuli Du1, Xiaohu Jiang2, Jinguan Lin3
1College of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, China. duxiuli@njnu.edu.cn.
Psychometrika
|June 2, 2023
概括
这项研究引入了一个新的后勤回归模型来处理医疗大数据中缺失的数据. 该方法有效地提取用于分类的关键信息,使用归算的功能主要组件分数和正规分数.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 在医疗保健中,多源功能块智能的缺失数据越来越常见.
- 现有的尺寸缩小方法通常将高维数据视为共变量,从而限制了它们在分类中的应用.
研究的目的:
- 提出一个新的多项式假定因子逻辑回归模型,用于对多源功能区块智能的缺失数据进行分类.
- 开发高效的尺寸缩小技术,从复杂的医疗数据集中提取重要信息.
主要方法:
- 在可观测数据上的单变函数主要组件分析 (FPCA).
- 使用有条件平均值和多个区块智能的归算,归算缺失的功能主要组件分数.
- 构建多源主要组件分数和正规分数的构建.
- 建立一个多项式的归算因子逻辑回归模型.
主要成果:
- 拟议的模型有效地处理多源功能区块智能的缺失数据.
- 假定的功能主要组件分数和正规分数作为有效的共同变量.
- 数字模拟和现实数据分析 (ADNI) 证明了该方法的有效性.
结论:
- 新的多项假定因子逻辑回归模型为复杂的缺失数据模式的分类问题提供了强大的解决方案.
- 归算策略和尺寸缩小技术对于在医疗大数据中准确地提取信息至关重要.
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