Software geniuses Ben and Saba have uploaded the backend of our online computing project here on GitHub. World-class JavaScript and Python skills, guys! ♔
Data and Software for Brain Imaging Analysis
Prediction With Maximum Regularized Likelihood Estimators
We have uploaded a paper titled “Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications” here on arXiv. We discuss “slow” rates for MRLEs in a wide range of settings. Cool stuff, Rui! ✌
STAT 311 has started
Find information about the course STAT 311 on Canvas and in the teaching section.
Inference for high-dimensional nested regression
We have established inference for two-stage regression models, with both stages high-dimensional. The paper is available here. Awesome work, David! ⛰
GitHub Repository
Find our new public GitHub page here.
Integrating Additional Knowledge Into Graph Estimation
We have developed an approach for integrating additional knowledge into parameter estimation in graphical models. The main idea is to funnel the knowledge into the tuning parameters. Find the paper here. Well done, Yunqi! 😁
RRF Grant Awarded
Johannes, Jing, and David have been awarded with a grant from the UW Royalty Research Fund (RRF) for their Big Data research in Econometrics. Second hit: it pays to work with Jing and David… 🍸
STAT 582 Upcoming
Important dates and further information about the course STAT 582 have been uploaded in the teaching section. Note the change of location to LOW 101.
A practical scheme for Lasso calibration
Our paper on tuning parameter calibration for the Lasso has been accepted at JMLR. You can find an updated version here.
Johannes Lederer 