Stat 153.
Main problem of this course is that I do not really know the material. The advanced
material is okay because it has a solid theoretical basis. But the early stuff is ad hoc and
case dependent – the worst type of material to learn. Also the students will need
experimental data to explore the models – ideally data that corresponds to realistic
situations, so where do I get this from? Also there is the infamous R – though probably it
is okay if Jose gives them a basic introduction and they use the manual for the rest.
Material to be covered.
Lectures 1 & 2: Chapter 1 of Little & Rubin.
This seems fairly straightforward. Though it contains a lot of case by case study.
Main points – there is missing data. Data a_{ij}. Index m_{ij} Different ways that data
can be missing. Mechanisms that lead to missing data. Missing Completely at
Random(MCAR). Missing at Random (MAR). Taxonomy of Missing Data Methods.
Probably enough for two lectures.
Lecture 3 : Chapter 2 of Little & Rudin.
Discussion of Least Squares and Yates Method.
Estimate quantities and the variance of the estimate.
Probably one lecture.
Lecture 4: Chapter 3 of Little & Rudin
Complete-Case and Available-Case analysis.
I do not really understand the weighting methods. They seem ad hoc.
Probably one lecture.
Lecture 5: Chapter 4 of Little & Rudin.
Single Imputation Methods – fill in the missing data. Mean/Regression/Stochastic
regression imputation.
Lecture 6: Chapter 5 of Little & Rudin.
Estimates of Imputation Uncertainty. Bootstrap & Jacknife.
Lecture 7 & 8: Chapter 6 of Little & Rudin.
Likelihood Functions Methods. Review of standard ML methods (one lecture). Extension
to cases with missing data. (MAR) versus non-MAR.
Lecture 9 & 10 Chapter 8 of Little & Rudin.
Expectation-Maximization Algorithm + Examples.
Lecture 11. Chapter 9 of Little & Rudin.
Standard errors based on the information matrix.
Lecture 12 & 13. Chapter 10 of Little & Rudin.
Data Augmentation and Mulitple Imputation.
Lecture 14, 15, 16. Examples. Chapters 11 –12.
Lecture 17, 18. Pearl’s work.
Lecture 19. Review.