在样本分割后,在高维通用线性模型中进行估计和推断的偏差拉索.
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
概括
这项研究引入了一个 debiased lasso 方法,用于高维通用线性模型的随机样本分割. 该方法通过减少偏差和差异来提高估计准确性,优于现有方法.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 高维通用线性模型 (GLMs) 在估计和推断方面存在挑战.
- 高维的变量选择和模型拟合往往遭受偏差和差异问题.
研究的目的:
- 开发和评估一种可靠的估计和推断方法,在高维的GLM中使用随机样本分割进行估计和推断.
- 提高复杂数据集的统计建模的准确性和效率.
主要方法:
- 采用随机样本分割来将数据分成培训和测试子样本.
- 使用LASSO (最小绝对收缩和选择运算符) 进行初始子模型选择.
- 在剩余的子样本上应用一个被删除的LASSO来适应所选模型.
- 研究了估计的非对称正常性和多重分割对效率的影响.
主要成果:
- 拟议的样本分割程序使用非定位的LASSO产生了非对称的正常估计值.
- 多重分割有效地解决了单一分割中固有的效率损失.
- 与标准的最大概率方法相比,减偏的LASSO显著降低了偏差和差异.
- 多重分割无基LASSO方法表现出比现有方法更优越的数值性能.
结论:
- 随机样本分割与无基质 LASSO 结合,为高维的 GLM 提供了一个强大的工具.
- 该方法在高维设置中提供了更可靠和更高效的估计.
- 该方法通过使用现实世界吸烟数据分析成功地说明了这一方法.
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