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Models for Missing Data

The lecture slides are here and a handout for one-page-at-a-time (color) printing is here. 

Lecture 9

"Models for Missing Data" covers the following topics:

  1. Overview
  2. Missingness Assumptions
  3. Application Specific Methods
  4. Multiple Imputation
  5. Computational Algorithms
  6. What Can Go Wrong
  7. Time Series, Cross-Sectional Imputations