JSM 2004 - Toronto

Abstract #300599

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Activity Number: 348
Type: Luncheons
Date/Time: Wednesday, August 11, 2004 : 12:30 PM to 1:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300599
Title: Missing Data in Regression Models - SOLD OUT
Author(s): Joseph G. Ibrahim*+
Companies: University of North Carolina, Chapel Hill
Address: Dept. of Biostatistics, Chapel Hill, NC, 27599,
Keywords: EM algorithm ; Gibbs sampling ; maximum likelihood ; missing at random ; nonignorably missing ; sensitivity analyses
Abstract:

Missing data is a major issue in many applied problems, especially in the biomedical sciences. We will discuss four common approaches for inference in missing data problems. These are i) Maximum Likelihood (ML), ii) Multiple Imputation (MI), iii) Weighted Estimating Equations (WEE), and Fully Bayesian (FB) methods. There is considerable interest as to how these four methodologies are related, the properties of each approach, the advantages and disadvantages of each methodology, computational implementation, software, and practical considerations of the methodologies. We will examine data that is missing at random (MAR) and nonignorably missing. Issues regarding model identifiability, model assessment, sensitivity analyses, and robustness will also be discussed.

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