Daily schedule

  1. 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
  2. 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
  3. 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
  4. 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