缺少数据
插补(统计学)
逆概率加权
加权
统计
计算机科学
反概率
数据挖掘
案例分析
计量经济学
数学
人工智能
贝叶斯概率
后验概率
估计员
医学
放射科
作者
Roderick J. A. Little,James R. Carpenter,Katherine J. Lee
标识
DOI:10.1177/00491241221113873
摘要
Missing data are a pervasive problem in data analysis. Three common methods for addressing the problem are (a) complete-case analysis, where only units that are complete on the variables in an analysis are included; (b) weighting, where the complete cases are weighted by the inverse of an estimate of the probability of being complete; and (c) multiple imputation (MI), where missing values of the variables in the analysis are imputed as draws from their predictive distribution under an implicit or explicit statistical model, the imputation process is repeated to create multiple filled-in data sets, and analysis is carried out using simple MI combining rules. This article provides a non-technical discussion of the strengths and weakness of these approaches, and when each of the methods might be adopted over the others. The methods are illustrated on data from the Youth Cohort (Time) Series (YCS) for England, Wales and Scotland, 1984–2002.
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