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This is the preliminary program for the 2009 Joint Statistical
Meetings in Washington, DC.
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The views expressed here are those of the individual authors and not necessarily those of the ASA or its board, officers, or staff. Back to main JSM 2009 Program page |
= Applied Session,
= Theme Session,
= Presenter| CE_20C | Mon, 8/3/09, 8:30 AM - 5:00 PM | RH-Renaissance Ballroom West B |
| Multiple Imputation of Missing Data - Continuing Education - Course | ||
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ASA |
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| Instructor(s): Paul D. Allison, University of Pennsylvania | ||
| This course covers both the conceptual foundations and practical details of implementing multiple imputation. Conventional methods for handling missing data typically yield biased estimates and/or incorrect standard errors. By contrast, multiple imputation produces estimates that have nearly optimal properties, under weaker assumptions. This course begins with an explanation of the assumptions of "missing at random" and "missing completely at random." After a brief review of conventional methods, multiple imputation based on linear regression with random draws will be considered. Implementation using the MCMC algorithm in SAS PROC MI will be examined in detail. Later topics include the role of the dependent variable, imputation under a restricted range, imputation of categorical variables, multivariate inference, interactions and nonlinearities, congeniality of data model and imputation model, longitudinal data, nonignorable missing data, and imputation by chained equations. The last topic will be demonstrated using the ice command in Stata. | ||
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JSM 2009
For information, contact jsm@amstat.org
or phone (888) 231-3473. If you have questions about the Continuing Education program,
please contact the Education Department. |