Course overview

  1. Session, .3 feb:

    • Recap of elements of linear models etc. Includes model matrices, QR decomposition and Cholesky decomposition, spectral theorem.
    • The shoes data.
    • Introduction to caracas.
    • To read: recap notes

  2. Session, 7. feb :

    • Weighted least squares
    • Likelihood analysis
    • One-way anova with random effect
    • Sum of squares
    • To read: recap notes, M&T section 5.1-5.4

  3. Session, 10. feb :

    • Mixed models in matrix form
    • The multivariate normal distribution
    • To read: mixed_notes sect 1-4

  4. Session, 17. feb :

    • Logistic regression
    • Random regression
    • Hypothesis test - \(\chi^2\)-test, F-test; bootstrap tests.
    • Prediction
    • Model checking
    • To read: mixed_notes sections on hypothesis test and random regression
    • To read: Helle Sørensens notes; sections on hypothesis test and random regression (the random regression part appears in several places)

  5. Session, 7. mar :

    • Hierarchical models
    • Split plot
    • Singular normal distribution
    • Miniproject

  6. Session, 14. mar :

    • Bayesian statistics

  7. Session, 21. mar :

    • Bayesian statistics

  8. Session, 28. mar :

    • Bayesian statistics

  9. Session : 4. apr

    • Generalized estimating equations
    • Generalized linear mixed models

  10. Session :

  • Generalized estimating equations
  • Generalized linear mixed models
  1. Session
  • Generalized estimating equations
  • Generalized linear mixed models
  1. Session
  • To be decided

Resources

Lecture notes

  1. Lecture notes by Helle Sørensen (KU) as reference material.

  2. Lecture notes by Søren Højsgaard. Will be continuously updated.

Recap

  1. Mainly used for the first two sessions, but additions on topics related to core content of the course will be made along the way.

Exercises

  1. A document with exercises. Will be updated along the way.

Programming

  1. Scripts and other programming things

caracas tutorial

  1. Tutorial on the caracas package for computer algebra in R.

Supplementary material

  1. Odds and ends