Date 
Topic 
Subject 
Book 
Code 
125 
Classical problems 
Introduction; problems with classical methods 
1.11.4 
R 
127 
Large scale testing 
Familywise error rates 

R 
201 
Large scale testing 
False discovery rates 

R 
203 
Large scale testing 
Local false discovery rates 

R 
208 
Large scale testing 
Hierarchical models and shrinkage 

R 
210 
Ridge regression 
Ridge regression 
1.5.11.5.3 
R 
215 
Ridge regression 
Selection of λ; case studies 
1.5.41.5.6 
R 
217 
Lasso 
KKT conditions, soft thresholding 
2.12.2 
R 
222 
Lasso 
Algorithms 
2.32.4 
R 
224 
Lasso 
Crossvalidaton 
2.52.6 
R 
301 
Lasso 
Case studies, Bayesian interpretation 
2.72.9 
R 
303 
Bias reduction 
Adaptive lasso, MCP, and SCAD 
3.13.2 
R 
308 
Bias reduction 
Algorithms; convexity 
3.53.7 
R 
310 
Bias reduction 
Case studies 
3.8 
R 
315 
Stability 
Elastic Net 
4.14.2 
R 
317 
Stability 
Algorithms; case studies 
4.34.5 
R 
322 
Theory 
Theoretical properties 
5 

324 
Theory 
Theoretical properties: Nonasymptotic 
5 

329 
Inference 
Marginal false discovery rates 
6 
R 
331 
Inference 
Debiasing and subsampling/resampling 
7, 9 
R 
405 
Inference 
Selective inference 

R 
407 
Inference 
Knockoff filter 

R 
412 
Other likelihoods 
Logistic regression 
10 
R 
414 

Instructional break, no class 


419 
Other likelihoods 
Logistic regression (cont’d) 
10 
R 
421 
Other likelihoods 
Other likelihoods 
1112 
R 
426 
Structured sparsity 
Group lasso 
13 
R 
428 
Structured sparsity 
Bilevel selection 
14 
R 
503 
Structured sparsity 
Fusion penalties 
15 
R 
505 
Structured sparsity 
Further applications of penalization and sparsity 

R 