JSM 2012 Online Program

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CE_15C	Mon, 7/30/2012, 8:30 AM - 5:00 PM	HQ-Indigo E
Statistical Analysis with Missing Data — Continuing Education Course
ASA , Biometrics Section
Instructor(s): Roderick Little, University of Michigan/U.S. Census Bureau, Trivellore Raghunathan, University of Michigan
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 and imputation; weighting methods; maximum likelihood and Bayesian inference with missing data; multiple imputation; 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. Recommended text: Little, R.J. and Rubin, D.B. (2002), Statistical Analysis with Missing Data, 2nd edition, Wiley.

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