
Lectures:
 Lecture 1:
Global testing, Bonferroni's global test, Fisher's combination test
 Lecture 2:
Global testing, optimality of Bonferroni's method for single
strong effect
 Lecture 3: Global testing,
chisquare test, optimality of chisquare test for distributed
mild effects
 Lecture 4: Global testing,
Simes test, Tests based on the empirical cumulative
distribution: KolmogorovSmirnov, Cramervon Mises,
AndersonDarling and Tukey's secondlevel significance testing
 Lecture 5: Multiple
testing/comparison problems; FWER, procedures for controlling
FWER, Holm's stepdownn procedure
 Lecture 6: Multiple
testing/comparison problems; closure principle, stepup and
stepdown procedures, Hochberg's procedure.
 Lecture 7: Multiple
testing/comparison problems; false discovery rate (FDR), properties of
FDR, procedure for controlling FDR (BH(q))
 Lecture 8: FDR, empirical
process viewpoint on BenjaminiHochberg procedure, Storey's correction
 Lecture 9: FDR control under
dependence.
 Lecture 10: FDR control under
positive dependence, the PRDS property
 Lecture 11: Multiple
testing/comparison problems; empirical Bayes approach to
multiple testing; empirical Bayes interpretation of
BH(q)
 Lecture 12: Knockoff filter,
FDR control under correlations
 Lecture 13: Modelfree
inference and selection, modelfree knockoffs
 Lecture 14: Modelfree
inference and knockoffs construction for genomewide association
studies (GWAS)
 Lecture 15: Selective
inference, False coverage rate (FCR), FCRadjusted confidence
intervals.
 Lecture 16: Selective
inference: postselection inference
(POSI), inference after model selection via the lasso
 Lecture 17: Selective
hypothesis tests, verifying the winner
 Lecture 18: Estimation of a
multivariate normal mean; Stein's phenomenon,
JamesStein estimate, Stein's unbiased risk estimate
 Lecture 19: Estimation of a
multivariate normal mean; Empirical Bayes Interpretation
of JamesStein
 Lecture 20: Prediction error, Stein's unbiaded risk estimate, Cp statistic
 Lecture 21: Model selection
with Cp
 Lecture 22: SURE and prediction error
for the lasso
 Lecture 23: Ideal risk,
oracles and risk inflation criterion
 Lecture 24: Risk inflation
of thresholding rules, excess risk of the Lasso
 Lecture 25: Adapting to
unkmown sparsity: FDR thresholding, Sorted L1 penalized estimation
(SLOPE)
Last year
lectures:
2016 Lectures

