Missing Data, Censored Data, and Multiple Imputation


Missing data and censored data are often present in observational studies, such as Customer Value Analysis (CVA), and designed experiments, such as in lifetime testing for quality improvement. Analyzing missing and/or censored data is challenging because, in addition to the problems that arise in analyzing complete data, it requires a model of missing-data mechanism and new software for fitting models and analyzing results. The latter often creates a tremendous programming burden, because analyzing incomplete data typically requires iterative methods, such as the EM algorithm for maximum likelihood estimation and MCMC methods for Bayesian estimation.

Our research program in this area takes two main approaches.

  1. Direct Modelling: Such examples include

  2. Multiple Imputation: The technique of Multiple Imputation of Rubin (1987) provides a simple way of analyzing incomplete data. For example,


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