Detailed Course Information

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STAT 890AG - Statistical Analysis with Missing Data
Missing data is a major issue in statistical analysis. This course introduces the four common approaches for inference in models with missing values, including maximum likelihood, multiple imputation, fully Bayesian, and weighted estimating equations. Computational tools (e.g. the EM algorithm and the Gibbs' sampler) will be discussed. 3.000 Credit hours 0.000 TO 3.000 Lecture hours 0.000 TO 3.000 Other hours Levels: Graduate Schedule Types: Lecture, Directed Reading, Examination Mathematics & Statistics Department Restrictions: Must be enrolled in one of the following Levels: Graduate

STAT 890AG - Statistical Analysis with Missing Data

Missing data is a major issue in statistical analysis. This course introduces the four common approaches for inference in models with missing values, including maximum likelihood, multiple imputation, fully Bayesian, and weighted estimating equations. Computational tools (e.g. the EM algorithm and the Gibbs' sampler) will be discussed.
3.000 Credit hours
0.000 TO 3.000 Lecture hours
0.000 TO 3.000 Other hours

Levels: Graduate
Schedule Types: Lecture, Directed Reading, Examination

Mathematics & Statistics Department

Restrictions:
Must be enrolled in one of the following Levels:
Graduate

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Detailed Course Information

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