Math Faculty Candidate: A multiple imputation method for incomplete correlated ordinal data using multivariate probit models

Speaker: Xiao Zhang, Research Scientist/Statistical Consultant, Department of Anesthesiology, Cedars-Sinai Medical Center

The multiple imputation technique has proven to be a useful tool in missing data analysis. We propose a Markov chain Monte Carlo method to conduct multiple imputation for incomplete correlated ordinal data using the multivariate probit model. We conduct a thorough simulation study to compare the performance of our proposed method with two available imputation methods - multivariate normal based and chain equation methods for various missing data scenarios. For illustration, we present an application using the data from the smoking cessation treatment study for low income community corrections smokers.


Friday, April 21 at 1:05 pm

Fisher Hall, 101
1400 Townsend Drive, Houghton, MI 49931

Event Type

Academics, Lectures/Seminars



Subscribing Departments ?

College of Sciences and Arts, Mathematical Sciences




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