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Topic: Mixed-Effects Models
Lecturer: | M. Kalisch | Time / Location: | Monday 15-17 at HG F3 |
Assistants: |
Manuel Koller, Jürg Schelldorfer |
Start: | Monday, February 22, 2010 |
Mixed-effects models play an increasingly important role in applied
statistics. In this seminar, we discuss linear, nonlinear and
generalized mixed-effects models emphasizing both theoretical
foundations and issues in applications.
As prerequisites, we suggest (not enforced):
Table of content and schedule.
1. Motivation and Introduction (Slides, Synopsis)
2. The Linear Mixed-Effects Probability Model (Slides, Synopsis)
3. Optimization Algorithms (Synopsis)
4. Hypothesis Tests, Confidence Intervals (Slides, Synopsis)
5. LMMs in Practice (Slides, Synopsis)
6. Prediction of New Observations (Slides, Synopsis, R-Code 1, R-Code 2)
7. Introduction to Nonlinear Regression (Slides, Synopsis)
8. Nonlinear Mixed-Effects Models (Slides, Synopsis)
9. Fitting Nonlinear Mixed-Effects Models (Slides, Synopsis)
10. Introduction to Generalized Linear Models (Slides, Synopsis, R-Code)
11. Generalized Linear Mixed-Effects Models (Synopsis, R-Code)
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