A note on multiply robust predictive mean matching imputation with complex survey data.
IF 1.2 4区 数学Q3 SOCIAL SCIENCES, MATHEMATICAL METHODSSurvey MethodologyPub Date : 2021-06-01Epub Date: 2021-06-24
Sixia Chen, David Haziza, Alexander Stubblefield
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引用次数: 0
Abstract
Predictive mean matching is a commonly used imputation procedure for addressing the problem of item nonrespone in surveys. The customary approach relies upon the specification of a single outcome regression model. In this note, we propose a novel predictive mean matching procedure that allows the user to specify multiple outcome regression models. The resulting estimator is multiply robust in the sense that it remains consistent if one of the specified outcome regression models is correctly specified. The results from a simulation study suggest that the proposed method performs well in terms of bias and efficiency.
期刊介绍：
The journal publishes articles dealing with various aspects of statistical development relevant to a statistical agency, such as design issues in the context of practical constraints, use of different data sources and collection techniques, total survey error, survey evaluation, research in survey methodology, time series analysis, seasonal adjustment, demographic studies, data integration, estimation and data analysis methods, and general survey systems development. The emphasis is placed on the development and evaluation of specific methodologies as applied to data collection or the data themselves.
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