Improving the coherence of sequential imputation via calibration Keywords: missing data, congeniality, multiple imputation, calibration, rounding Inference by multiple imputation(MI) is a popular method for handling missing data in many scenarios. One of the biggest obstacles facing practitioners of MI is the choice of the appropriate imputation model, particularly in applications where missingness is seen in variables of different nature. Sequential imputation or variable-by-variable imputation has been increasingly used in such settings. A major concern with this technique is the potential incoherence of the imputations with the underlying posterior predictive distribution. In this talk, I consider a calibration technique to improve the coherence of the sequential imputation. Calibration is a technique designed to preserve marginal distributions in the imputed data. Specifically, we impose a misspecified imputation model such as multivariate normal distribution and correct the adverse impact of this misspecification via calibration.
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Important Dates & Deadlines
- May 31, 2011
Registration Deadline for All Session Presenters - September 1, 2011
Poster Abstract Online Submission Closes - September 9, 2011
Hotel Reservations Close - September 15, 2011
Conference Registration Closes