Our paper “Inference for high-dimensional instrumental variables regression” has now been published. Congratulations again to David and Jing!
Author Archives: LedererLab
Statistical Guarantees for Deep Learning
We have derived statistical guarantees for deep learning here. Well done, Mahsa and Fang! ⚡⚡⚡
Calibrating the Graphical Lasso
We have established a new strategy for calibrating the graphical lasso here. Great work, Mike! 🍷
Lasso in Theory and Practice
We have uploaded a new paper on the lasso’s effective noise and on consequences for calibration and inference here. Thanks to Michael for the great collaboration!
FDR Control in Labor Economics
Our paper A Pipeline for Variable Selection and False Discovery Rate Control with an Application in Labor Economics has been accepted for presentation at the 2020 Annual Congress of the Swiss Society of Economics and Statistics. Congratulations to Sophie-Charlotte Klose! Very impressive!
Ridge Regression Without Tuning Parameters
We have uploaded a paper on tuning-free ridge regression here. Well done, Shih-Ting and Fang!
High-dimensional Inference
Our paper Inference for high-dimensional instrumental variables regression has been accepted by the Journal of Econometrics. Wonderful work from my co-authors Jing (faculty at University of Washington) and David (former student at University of Washington)! ✈
Personalized Medicine
We have uploaded a new paper on prediction in personalized medicine here. Congratulations to Shih-Ting and Yannick, who are the student authors of this paper!
Mike completed Master’s thesis
Mike Laszkiewicz has completed his Master’s thesis about Graphical Models. Congratulations! 🎓
Tuning Parameters in Logistic Regression
Our paper “Tuning parameter calibration for
l1-regularized logistic regression” has now been published in the Journal of Statistical Planning and Inference.
