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Statistical Analysis of Incomplete Data

(Same as Biostatistics M232.) Lecture, three hours; discussion, one hour. Requisites: Biostatistics 200C, 202B or equivalent. Sources of incomplete data, recognizing familiar methods as solutions to missing-data problems, missing-data mechanisms, weighting and imputation strategies, model-based and design-based inference, likelihood-based and Bayesian methods, statistical computing strategies, multivariate models for diverse data types, nonignorable models, review of available statistical software. Emphasis on incorporating incomplete-data perspective into broader statistical-science framework. S/U or letter grading.

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Instructor
Thomas R. Belin
Previously taught
20S 16S

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