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Award ID contains: 1733835

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  1. ; ; ; (, Journal of the Royal Statistical Society Series A: Statistics in Society)
    Abstract Often, government agencies and survey organizations know the population counts or percentages for some of the variables in a survey. These may be available from auxiliary sources, for example administrative databases or other high-quality surveys. We present and illustrate a model-based framework for leveraging such auxiliary marginal information when handling unit and item nonresponse. We show how one can use the margins to specify different missingness mechanisms for each type of nonresponse. We use the framework to impute missing values in voter turnout in a subset of data from the US Current Population Survey. In doing so, we examine the sensitivity of results to different assumptions about the unit and item nonresponse. 
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  2. ; (, Statistics in the Public Interest – In Memory of Stephen E. Fienberg)
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  3. ; ; (, Privacy in Statistical Databases)
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  4. ; (, Biometrika)
    Summary We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate the use of these mechanisms in an application. 
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