Treatment of nonignorable missing data when modeling unobserved heterogeneity with finite mixture models
Thomas Lehmann,
Peter Schlattmann
Abstract:Multiple imputation has become a widely accepted technique to deal with the problem of incomplete data. Typically, imputation of missing values and the statistical analysis are performed separately. Therefore, the imputation model has to be consistent with the analysis model. If the data are analyzed with a mixture model, the parameter estimates are usually obtained iteratively. Thus, if the data are missing not at random, parameter estimation and treatment of missingness should be combined. We solve both prob… Show more
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