Linear Regression Analysis With Missing Observations Among the Independent Variables

M. Glasser

doi:10.2307/2283101

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Linear Regression Analysis With Missing Observations Among the Independent Variables

M. Glasser刊期 1964-09 · 卷 59（307）Journal of the American Statistical Association已编入正刊

自变量存在缺失观测时的线性回归分析AI 解读 ↓

一般估计

被引 12 次 ↗OpenAlex · 核验 2026-10-04

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DOI · 10.2307/2283101

Journal of the American Statistical Association · 卷 59，期 307 · 页码 / 文章号 834

正式刊期日期：1964-09

DOI 首次登记：2006-05-10

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M. Glasser

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自变量存在缺失观测时的线性回归分析

研究问题: 当部分个体的自变量以不同数量和模式缺失时，如何估计线性回归系数，估计效率又受哪些因素影响？
方法与思路: 作者提出一种直观的回归系数估计方法，并分析自变量之间的相关程度及缺失观测比例与方法效率的关系。
材料陈述的贡献: 摘要称，给出适用于不同自变量缺失模式的估计思路，并指出其效率同时取决于变量相关性与缺失比例。
阅读关注点: 应核对估计量的具体构造、缺失机制要求、变量分布设定，以及效率比较使用的基准和衡量标准。
阅读边界: 所据为版本未注明的 OpenAlex 索引摘要；摘要未说明缺失机制、偏差或一致性，不能默认适用于非随机缺失。

GPT-6.1 Sol · 高 · 2026-10-05

原文摘要

An intuitive estimate is presented for estimating linear regression coefficients when an unspecified number and pattern of the independent variables for some individuals are not observed. It is shown that the efficiency of this approach depends upon the correlation among the independent variables as well as the proportion of observations which is missing.

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