Here is a big list of suggested potential papers/topics for a final project, roughly organized by topic. This is in no way meant to be a complete list – you are more than welcome to select a paper not on this list. But if you’re looking for ideas, this should give you an idea of the interesting and influential papers and ideas out there that we weren’t able to include in the course.
Multiple testing
- Covariate-adjusted FDR: Scott2015
- ashr, another way of estimating local FDR: Stephens2017
- Gene set testing: Leeuw2016
- Accuracy of FDR for correlated tests:
Cross-validation (and related ideas)
- Random-X vs Fixed-X regression: Rosset2020
- Cross-validation for correlated data: Rabinowicz2020
- What exactly is cross-validation estimating?: Bates2023
- Estimation of the error variance: Reid2016
- Conformal prediction: Shafer2008 and Lei2018
Miscellaneous lasso variants
Bi-level variable selection
- Group exponential lasso: Breheny2015
- Exclusive lasso: Campbell2017
Power, sample size, and study design
- For developing classifiers: Sanchez2016
- For large-scale testing: Pawitan2005
Algorithms
- ADMM: Zhu2017
- Proximal methods: Polson2015
- Strong rules: Tibshirani2012
- L0Learn: Hazimeh2020 and Hazimeh2023
Graphical models
- Mixed (discrete + continuous): Lee2015
- Graphical lasso: Mazumder2012
Interactions
- Pliable lasso Tibshirani2020
- Pairwise fusion for subgroup analysis: Ma2017
Deconfounding and linear mixed models
- A review of the area: Bulhmann2020
- Penalized LMMs: Rakitsch2013
- Spectral deconfounding: Cevid2020
- Lava: Chernozhukov2017
Matrix completion
- Compressed sensing: Willett2011
- SOFT-IMPUTE: Mazumder2010
Inference
- Bootstrapping Rinaldo2019
- Gaussian mirror: Xing2023 and Dai2023a
- Data fission: Leiner2024
Miscellaneous other neat papers
- Can ridge perform variable selection?: Wu2020
- Lassoed principal components: Witten2008
- Cooperative lasso: Ding2022
- Higher criticism: Donoho2015