Journal of the American Statistical Association · 1982 · 34 citations · 0 references
Public HealthBiostatisticsData AnalyticsEconometricsStatistical ModelingCensoring MechanismsCount Data ModelsEstimation TheoryMissing Data HandlingMissing Data ImputationMissing Data MethodsData ImputationEstimation Under CensoringHandling Missing DataImputation Of EarningsMissing DataMissing Data AnalysisMissing Data InferenceMissing Value Imputation
Abstract A method is developed for imputing missing values when the probability of response depends upon the variable being imputed. The missing data problem is viewed as one of parameter estimation in a regression model with stochastic censoring of the dependent variable. The prediction approach to imputation is used to solve this estimation problem. Wages and salaries are imputed to non-respondents in the Current Population Survey and the results are compared to the nonrespondents' IRS wage and salary data. The stochastic censoring approach gives improved results relative to a prediction approach that ignores the response mechanism. Key Words: NonresponseImputationPrediction approachCensoringCurrent Population Survey