## Daily schedule

- High-dimensional testing (
*Claus Ekstrøm*)
**To read:** Chapter 15.
- Big-p small-n problems
- Multiple testing techniques (inference correction, false discovery rates, q-values)
- Randomziation, permutation and bootstrap testing

- Statistical analysis with missing data (
*Claus Ekstrøm*)
**To read:** The following paper on multiple imputation: Multiple imputation 1
- Concepts in missing data analysis: MCAR, MAR, MNAR
- Pitfalls in naive imputation
- Rubin’s rules
- Multiple imputations by chained equations

- Random forests (
*Thomas Gerds* and *Anne Lyngholm*)
**To read:** Chapter 8 (in particular 8.4), Chapter 17
- Modeling cultures
- Decision trees
- Random forests
- Variable importance

- Introduction to statistical methods for high-dimensional data, linear models, regularization methods, and variable selection (
*Benjamin Skov Kaas-Hansen*)
**To read:** Chapter 8 (in particular 8.1-8.3). Flip through Chapters 10, 11, 16, and 20.
- Generalized linear models refresher
- Penalized regression (lasso and elastic net)
- Parametric and non-parametric bootstrap
- Cross-validation

Last updated 2021