Statistics4MR-2022-23 — различия между версиями
Bdemeshev (обсуждение | вклад) |
Bdemeshev (обсуждение | вклад) |
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[https://github.com/xenakas/stat4mr_2022/tree/main/lecture_notes Lecture handwritten notes] | [https://github.com/xenakas/stat4mr_2022/tree/main/lecture_notes Lecture handwritten notes] | ||
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+ | [https://github.com/xenakas/stat4mr_2022/raw/main/ha/stat4mr.pdf HA1]: deadline: 2022-12-06, 21:00. | ||
'''Week 1''' | '''Week 1''' |
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