We have uploaded two new papers on FDR control here and here on arXiv. Great job, Fang, Lu, and Tobias!
Category Archives: New Paper
Prediction Error Bounds for Linear Regression With the TREX
We have uploaded a new paper that contains theoretical insights about our TREX here on arXiv. Thanks to my collaborators Jacob, Irina, and Christian!
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! ✌
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! ⛰
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! 😁
Tuning Parameter Calibration in High-dimensional Logistic Regression With Theoretical Guarantees
We have extended the AV-scheme for tuning parameter calibration to logistic regression. Our approach provides efficient feature selection and is supported by theoretical guarantees. The paper can be downloaded here. Great job, Wei! 😎
Efficient Feature Selection With Large and High-dimensional Data
After presenting it in a couple of talks, we have now submitted the manuscript about efficient feature selection. In particular, we introduce a simple, yet effective algorithm based on Lasso optimization steps. The paper is available here. Congrats to Néhémy! 🙂
Graphical Models for Discrete and Continuous Data
We have uploaded a paper on graphical models. In this paper, we introduce a general framework that allows for discrete, continuous, and combined types of data. Find the manuscript here.
Oracle Inequalities for High-dimensional Prediction
We have just uploaded a new paper on high-dimensional prediction. It provides bounds for a wide range of penalized estimators – importantly, without making assumptions on the design matrix. Check it out here.
