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Statistics4MR-2022-23 - История изменений
2026-06-06T14:43:23Z
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2022-11-24T09:37:59Z
<p>Migrated current public revision from wiki.cs.hse.ru</p>
<p><b>Новая страница</b></p><div>== Plan ==<br />
<br />
Нажми "развернуть", чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣<br />
<div class="mw-collapsible mw-collapsed" style="width:1000px; overflow: hidden;"><br />
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=== Module 1 ===<br />
<br />
(1 lecture + 1 class per group X 7 weeks)<br />
<br />
Bootstrap + Jacknife + CV<br />
Sampling<br />
Welch test<br />
Mann-Whitney test<br />
Sample size calculation<br />
Multiple comparison corrections<br />
<br />
=== Module 2 ===<br />
<br />
(1 lecture + 1 class per group X 8 weeks)<br />
CUPED<br />
Difference in Difference estimator<br />
Matching<br />
Contingency tables, Chi-squared tests<br />
ANOVA, ANCOVA<br />
Partial correlation + problem solving <br />
<br />
=== Module 3 ===<br />
<br />
(1 lecture + 1 class per group X 11 weeks)<br />
<br />
Discriminant analysis<br />
Logit<br />
PCA<br />
Factor analysis<br />
Cluster analysis, Dendrogramms<br />
Conjoint Analysis<br />
Multidimensional scaling<br />
<br />
</div><br />
<br />
==== Grades ====<br />
<br />
UoL grade = 0.7 * UoL exam + 0.3 * UoL coursework <br />
<br />
Internal grades: <br />
<br />
Fall grade = 0.4 * HA + 0.6 December Exam<br />
<br />
Final grade = 0.5 * Fall grade + 0.2 HA + 0.3 * Final exam<br />
<br />
[https://docs.google.com/spreadsheets/d/e/2PACX-1vQJv_9-5FgPZOVN8Kr3iCYqWVuHsOFro12SRdsQf90w214IJpGAETqOG6_EUwVux6OgCae0kgzmldQ2/pub?output=xlsx Actual grades]<br />
<br />
== Log-book ==<br />
<br />
[https://www.youtube.com/playlist?list=PLGpdGKp2JUvz4rUd_bvyBBmxArWJX0w6R Lecture recordings: youtube] <br />
<br />
[https://disk.yandex.ru/d/_Jj2yGpdqIXykg lecture recordings: ya-folder]<br />
<br />
[https://drive.google.com/drive/folders/1sEFIKasOituMMwqvkAGmTou1ctxIWOfn Seminar recordings]<br />
<br />
[https://github.com/xenakas/stat4mr_2022/tree/main/lecture_notes Lecture handwritten notes]<br />
<br />
[https://github.com/xenakas/stat4mr_2022/raw/main/ha/stat4mr.pdf HA1]: deadline: 2022-12-06, 21:00.<br />
<br />
'''Week 1''' <br />
<br />
Naive bootstrap, t-statistic bootstrap<br />
<br />
Sources of wisdom:<br />
<br />
[http://web.cecs.pdx.edu/~cgshirl/Documents/Demonstrations/1983%20Efron%20Gong%20A%20Leisurely%20Look%20at%20the%20Bootstrap%20Jackknife%20CV%20CV_Boot_Jack2685844.pdf Efron, Leisurely Look at bootstrap, jackknife and cv]<br />
<br />
[https://arxiv.org/abs/1411.5279 Tim Hesterberg, What Teachers Should Know about the Bootstrap?]<br />
<br />
'''Week 2'''<br />
<br />
Variance estimation: jackknife, bootstrap. Bootstrap in bootstrap.<br />
<br />
'''Week 3'''<br />
<br />
Examples of bootstrap failures, problem solving<br />
<br />
'''Week 4'''<br />
<br />
Welch test, Mann-Whitney test<br />
<br />
'''Week 5'''<br />
<br />
Sample size calculation exercises, MDE<br />
<br />
'''Week 6'''<br />
<br />
Multiple comparisons: FWER.<br />
<br />
<br />
'''Week 8'''<br />
<br />
Multiple comparisons: FDR.<br />
<br />
<br />
'''Week 9'''<br />
<br />
PCA+SVD: maximum variance approach<br />
<br />
'''Week 10'''<br />
<br />
PCA+SVD: average R2 maximization<br />
<br />
'''Week 11'''<br />
<br />
PCA+SVD: problem solving</div>
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