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Activity Details

CE_12C Sun, 7/30/2017, 8:30 AM - 5:00 PM H-Holiday Ballroom 6
Statistical Analysis with Missing Data (ADDED FEE) — Professional Development Continuing Education Course
ASA , Biometrics Section
This short course will discuss methods for the statistical analysis of data sets with missing values. Topics will include: Definition of missing data; assumptions about mechanisms, including missing at random; pros and cons of simple methods such as complete-case analysis, naïve imputation etc; Weighting methods; multiple imputation; maximum likelihood and Bayesian inference with missing data; computational techniques, included EM algorithm and extensions, and Gibbs sampler; software for handling missing data; missing data in common statistical applications, including regression, repeated-measures analysis, clinical trials. Selection and pattern-mixture models for nonrandom nonresponse. Prerequisites: Course requires knowledge of standard statistical models such as the multivariate normal, multiple linear regression, contingency tables, as well as matrix algebra, calculus, and basic maximum likelihood for common distributions.
Instructor(s): Roderick J Little, University of Michigan, Trivellore Raghunathan, University of Michigan School of Public Health
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