High-dimensional
data analysis, fall 2013
Lectures: Mondays 13:15 – 15:00 in MVH 11
Thursdays 13:15 – 15:00 in MVL 15
Wednesdays 13:15 – 15:00 in
? (reserve
time)
Course book:
“Statistics
for High-Dimensional Data. Methods, Theory and Applications”, P. Buhlmann and S. van de Geer, Springer 2011.
Complementary book:
”The Elements of Statistical
Learning”, T. Hastie, R. Tibshirani, J. Friedman,
Springer 2009
Course content:
•
Lasso
- linear models
- generalized linear models
- group
- smooth functions
•
P-values
•
Boosting
(probably not)
•
Graphical
models
•
Asymptotics
• Computation
Exercises:
2.1, 2.2, 2.3, 2.5, 2.8, 3.2, 3.3, 3.4, 3.5, 4.1, 5.1, 5.4, 5.5, HTF5.1, H:1, 6.1, 10.1
•
Oral exam (one random question on book/slide
+ one random question on an excercise + follow-up/other questions: 30
min to prepare, using all material, about 20 min for exam)
•
Project:
analyze a high-dimensional data set of your
own (if you don’t have one, Volvo might be able to provide), alone or
in groups. Examination by presentation of your project,
Monday, Nov. 25 or Thursday, Nov . 28 + handin of the
slides for your presentation.
•
A computation lab (probably cancelled)
Date |
Content |
Literature |
|
|
|
Thursd. 12/9 MVL 15 |
Introduction, lecture by José, the Lasso |
José’s slides B&vdG 1 - 2.3 Slides: Hdd1 |
Mond. 16/9 MVH 11 |
Prediction,
selection, asymptotics |
B&vdG 2.4 – 2.7, HTF
7.10 Slides: Hdd1 |
Thursd. 19/9 MVL 15 |
|
|
Mond. 23/9 MVH 11 |
generalized linear models group Lasso |
Slides: Hdd3, Hdd4 |
Thursd. 26/9 MVL 15 |
|
|
Mond. 30/9 |
No Lecture |
|
Wednesd. 2/10 |
problem solving by participants | Solved problems: 2.1, 2.2 2.3, 2.8, 3.2, 3.3, 3.4 Slides: Solutions1 |
Thursd. 3/10 MVL 15 |
|
|
Mond. 7/10 MVH 11 |
|
B&vdG 6.2 Slides: Hdd6 |
Wednesd. 9/10 |
|
|
Thursd. 10/10 |
proofs |
B&vdG 6.2 Slides: Hdd6 |
Mond. 14/10 |
No Lecture |
|
Thursd. 17/10
|
No Lecture |
|
Mond. 21/10
|
No Lecture |
|
Thursd. 24/10 MVL 15 |
discussion of projects |
|
Mond. 28/10 MVH 11 |
|
|
Thursd. 31/10 MVL 15 |
|
|
Mond. 4/11
|
No Lecture |
|
Thursd. 7/11
|
No Lecture |
|
Mond. 11/11 MVH 11 |
|
|
Thursd. 14/11 MVL 15 |
|
|
Mond. 18/11 MVH 11 |
participants |
|
Thursd. 21/11
|
No Lecture |
|
participants |
|
|
Thursd. 28/11 MVL 15 |
|
|
http://www-stat.stanford.edu/~tibs/statlearningsoft.html
Slides:
Conditional distributions for multivariate normal distribution (extracted from "Stationary Stochastic Processes for Scientists and Engineers", by Lindgren, Sandsten; Rootzen, http://www.crcpress.com/product/isbn/9781466586185)
Tobias Abenius "Fused elastic net EPoC"
José Sánchez "Gene Networks Estimation: Extensions of the lasso"
Artur Grzebowski & Henrike Häbel "PQS dissatisfaction survey: comparison OPLS/Lasso"