Statistics4MR-2022-23 — различия между версиями
Bdemeshev (обсуждение | вклад) |
Bdemeshev (обсуждение | вклад) |
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(не показано 14 промежуточных версии 2 участников) | |||
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− | + | == Plan == | |
− | + | Нажми "развернуть", чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣 | |
+ | <div class="mw-collapsible mw-collapsed" style="width:1000px; overflow: hidden;"> | ||
+ | |||
+ | === Module 1 === | ||
(1 lecture + 1 class per group X 7 weeks) | (1 lecture + 1 class per group X 7 weeks) | ||
Строка 12: | Строка 15: | ||
Multiple comparison corrections | Multiple comparison corrections | ||
− | + | === Module 2 === | |
(1 lecture + 1 class per group X 8 weeks) | (1 lecture + 1 class per group X 8 weeks) | ||
Строка 22: | Строка 25: | ||
Partial correlation + problem solving | Partial correlation + problem solving | ||
− | + | === Module 3 === | |
(1 lecture + 1 class per group X 11 weeks) | (1 lecture + 1 class per group X 11 weeks) | ||
Строка 34: | Строка 37: | ||
Multidimensional scaling | Multidimensional scaling | ||
− | + | </div> | |
+ | |||
+ | ==== Grades ==== | ||
UoL grade = 0.7 * UoL exam + 0.3 * UoL coursework | UoL grade = 0.7 * UoL exam + 0.3 * UoL coursework | ||
− | Internal grade = 0.4 * | + | Internal grades: |
+ | |||
+ | Fall grade = 0.4 * HA + 0.6 December Exam | ||
+ | |||
+ | Final grade = 0.5 * Fall grade + 0.2 HA + 0.3 * Final exam | ||
+ | |||
+ | [https://docs.google.com/spreadsheets/d/e/2PACX-1vQJv_9-5FgPZOVN8Kr3iCYqWVuHsOFro12SRdsQf90w214IJpGAETqOG6_EUwVux6OgCae0kgzmldQ2/pub?output=xlsx Actual grades] | ||
+ | |||
+ | == Log-book == | ||
+ | |||
+ | [https://www.youtube.com/playlist?list=PLGpdGKp2JUvz4rUd_bvyBBmxArWJX0w6R Lecture recordings: youtube] | ||
+ | |||
+ | [https://disk.yandex.ru/d/_Jj2yGpdqIXykg lecture recordings: ya-folder] | ||
+ | |||
+ | [https://drive.google.com/drive/folders/1sEFIKasOituMMwqvkAGmTou1ctxIWOfn Seminar recordings] | ||
+ | |||
+ | [https://github.com/xenakas/stat4mr_2022/tree/main/lecture_notes Lecture handwritten notes] | ||
+ | |||
+ | [https://github.com/xenakas/stat4mr_2022/raw/main/ha/stat4mr.pdf HA1]: deadline: 2022-12-06, 21:00. | ||
+ | |||
+ | '''Week 1''' | ||
+ | |||
+ | Naive bootstrap, t-statistic bootstrap | ||
+ | |||
+ | Sources of wisdom: | ||
+ | |||
+ | [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] | ||
+ | |||
+ | [https://arxiv.org/abs/1411.5279 Tim Hesterberg, What Teachers Should Know about the Bootstrap?] | ||
+ | |||
+ | '''Week 2''' | ||
+ | |||
+ | Variance estimation: jackknife, bootstrap. Bootstrap in bootstrap. | ||
+ | |||
+ | '''Week 3''' | ||
+ | |||
+ | Examples of bootstrap failures, problem solving | ||
+ | |||
+ | '''Week 4''' | ||
+ | |||
+ | Welch test, Mann-Whitney test | ||
+ | |||
+ | '''Week 5''' | ||
+ | |||
+ | Sample size calculation exercises, MDE | ||
+ | |||
+ | '''Week 6''' | ||
+ | |||
+ | Multiple comparisons: FWER. | ||
+ | |||
+ | |||
+ | '''Week 8''' | ||
+ | |||
+ | Multiple comparisons: FDR. | ||
+ | |||
+ | |||
+ | '''Week 9''' | ||
+ | |||
+ | PCA+SVD: maximum variance approach | ||
+ | |||
+ | '''Week 10''' | ||
− | + | PCA+SVD: average R2 maximization | |
− | + | '''Week 11''' | |
− | + | PCA+SVD: problem solving |
Текущая версия на 12:37, 24 ноября 2022
Содержание
Plan
Нажми "развернуть", чтобы просмотреть содержимое - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 🡣
Grades
UoL grade = 0.7 * UoL exam + 0.3 * UoL coursework
Internal grades:
Fall grade = 0.4 * HA + 0.6 December Exam
Final grade = 0.5 * Fall grade + 0.2 HA + 0.3 * Final exam
Log-book
HA1: deadline: 2022-12-06, 21:00.
Week 1
Naive bootstrap, t-statistic bootstrap
Sources of wisdom:
Efron, Leisurely Look at bootstrap, jackknife and cv
Tim Hesterberg, What Teachers Should Know about the Bootstrap?
Week 2
Variance estimation: jackknife, bootstrap. Bootstrap in bootstrap.
Week 3
Examples of bootstrap failures, problem solving
Week 4
Welch test, Mann-Whitney test
Week 5
Sample size calculation exercises, MDE
Week 6
Multiple comparisons: FWER.
Week 8
Multiple comparisons: FDR.
Week 9
PCA+SVD: maximum variance approach
Week 10
PCA+SVD: average R2 maximization
Week 11
PCA+SVD: problem solving