Pinned Repositories
ATbounds-r
Bounding Treatment Effects by Pooling Limited Information across Observations
persuasio-stata
Estimating the Effect of Persuasion in Stata
replication-JunLee-JPE
Replication files for Jun and Lee (2022)
SGDinference
R package for SGD inference
Best-Subset-Binary-Prediction
Matlab codes for the best subset binary prediction method
Best-Subset-Binary-Prediction-py
ciccr
Causal Inference in Case-Control Studies
llss-2018-jasa
persuasio
Estimating the Effect of Persuasion in Stata
sketching
Sketching of Data via Random Subspace Embeddings
sokbae's Repositories
sokbae/sketching
Sketching of Data via Random Subspace Embeddings
sokbae/ciccr
Causal Inference in Case-Control Studies
sokbae/llss-2018-jasa
sokbae/persuasio
Estimating the Effect of Persuasion in Stata
sokbae/Best-Subset-Binary-Prediction
Matlab codes for the best subset binary prediction method
sokbae/Best-Subset-Binary-Prediction-py
sokbae/Dimension-reducing-conditional-moment-inequalities
Gauss codes for replicating the simulation results of Chen and Lee (2019) on dimension reducing conditional moment inequalities for discrete choice models
sokbae/fadtwo
FActor-Driven TWO-regime regressions
sokbae/illinois-wellness-data
Public use data for the Illinois Workplace Wellness Study
sokbae/IVQR-GMM-computation-codes
Matlab codes for exact computation of IVQR GMM estimators
sokbae/IVQR-GMM-Python
sokbae/ivqrgmm-r
sokbae/L0-ERM
Matlab codes for the L0-ERM binary classification method
sokbae/llss-rz
sokbae/Matlab-data-coll
This project provides Matlab commands for covariate and sample size selection in randomized control trials
sokbae/prescience-r
R package of the Approximate Best Subset Maximum Binary Prediction Rule (PRESCIENCE) proposed by Chen and Lee (2018).
sokbae/replication-JunLee-2023-AISTATS
sokbae/replication-JunLee-JBES
sokbae/replication-LeeNg-2022-ICML
Replication Files for Lee and Ng (2022, ICML)
sokbae/sampleproject
A sample project that exists for PyPUG's "Tutorial on Packaging and Distributing Projects"
sokbae/sparseHP
Sparse HP and other filtering procedures proposed in Lee et al. (2020)
sokbae/SparseQR
Matlab codes for L0-based regularized quantile regression methods