高维度,取决于结果的缺失数据问题:人类位置模型
Lars Leonardus Joannes van der Burg1, Hein Putter1, Henning Baldauf2
1Biomedical Data Sciences, LUMC, Leiden, The Netherlands.
将结果模型纳入KIR双型缺失数据归算中可以引入偏差. 没有结果建模的基线预期最大化算法通常表现更好或可比,特别是在高维生物数据中.
科学领域:
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 缺失的数据在高维生物数据集中普遍存在.
- 输入和预期最大化 (EM) 算法用于数据重建.
- 将回归模型集成到归算中可能会减少回归系数偏差.
研究的目的:
- 评估基于结果的EM算法,用于重建缺少数据的KIR双型.
- 将结合高维回归模型的策略与基线EM算法进行比较.
主要方法:
- 扩展了之前提出的EM算法,包括一个高维回归模型.
- 评估了三种策略:仅对等位基预测者,对等位基预测者与类型选择,并处罚回归.
- 通过模拟将这些策略与没有结果模型的基线EM算法进行了比较.
主要成果:
- 基于结果的EM算法在效果大小和缺失的极端场景中表现优于基线.
- 在大多数情况下,基线EM算法的性能优于或相似.
- 包含一个结果模型可能会引入有害影响和偏见.
结论:
- 基于结果的缺失数据模型在高维设置中需要仔细应用.
- 这些模型可能会导致偏见的结果,特别是在重建KIR双型时.
- 没有结果建模的基线EM算法通常是一种更强大的方法.
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