Missing Data
*Joseph L. Schafer, Department of Statistics 

Keywords: latent class, multiple imputation, nonignorable, selection model

Data are said to be nonignorably missing if the probabilities of missingness depend on unobserved quantities. Traditional election models for nonignorable nonresponse are outcome-based, tying these probabilities to the partially observed values directly e.g., by a logistic regression) and can be quite unstable. With multivariate responses, the number of distinct missingness patterns ecomes large, making outcome-based selection modeling unattractive. In some examples, however, the information in the binary missing-data indicators is well summarized by a simple latent-class structure, suggesting that relationships between these indicators and the partially observed items might be explainable by the latent classes. In this talk, we present a latent-class selection model for multivariate incomplete data and investigate its properties. We then compare multiple imputations drawn from

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For more information send email: ichps@amstat.orgHPSS 2005