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This is the preliminary program for the 2008 Joint Statistical
Meetings in Denver, Colorado.
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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 2008 Program page |
= Applied Session,
= Theme Session,
= Presenter|
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| CE_26C | Tue, 8/5/08, 8:30 AM - 5:00 PM | CC-205 |
| 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 will cover 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 with nearly optimal properties under weaker assumptions. I will explain the assumptions of "missing at random" and "missing completely at random." After a brief review of conventional methods, we will consider multiple imputation based on linear regression with random draws. We will examine implementation using the MCMC algorithm in SAS PROC MI in detail, and then move on to 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 (demonstrated using the ice command in Stata). | ||
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JSM 2008
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. |