166 citations · 424 across the 44 of their papers we have counts for
11 papers · 1 filter
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming
Somnath Chakraborty, Johannes Lederer, Rainer von Sachs
Motivated by a variety of applications, high-dimensional time series have become an active topic of research. In particular, several methods and finite-sample theories for individu…
Cardinality Sparsity: Applications in Matrix-Matrix Multiplications and Machine Learning
Ali Mohades, Johannes Lederer
High-dimensional data has become ubiquitous across the sciences but presents computational and statistical challenges. A common approach to addressing these challenges is through s…
Prediction Error Bounds for Linear Regression With the TREX
Jacob Bien, Irina Gaynanova, Johannes Lederer +1
The TREX is a recently introduced approach to sparse linear regression. In contrast to most well-known approaches to penalized regression, the TREX can be formulated without the us…
Inference for high-dimensional instrumental variables regression
David Gold, Johannes Lederer, Jing Tao
This paper concerns statistical inference for the components of a high-dimensional regression parameter despite possible endogeneity of each regressor. Given a first-stage linear m…
Graphical Models for Discrete and Continuous Data
Rui Zhuang, Noah Simon, Johannes Lederer
We introduce a general framework for undirected graphical models. It generalizes Gaussian graphical models to a wide range of continuous, discrete, and combinations of different ty…
Oracle Inequalities for High-dimensional Prediction
Johannes Lederer, Lu Yu, Irina Gaynanova
The abundance of high-dimensional data in the modern sciences has generated tremendous interest in penalized estimators such as the lasso, scaled lasso, square-root lasso, elastic…
On the Prediction Performance of the Lasso
Arnak S. Dalalyan, Mohamed Hebiri, Johannes Lederer
Although the Lasso has been extensively studied, the relationship between its prediction performance and the correlations of the covariates is not fully understood. In this paper,…
The Group Square-Root Lasso: Theoretical Properties and Fast Algorithms
Florentina Bunea, Johannes Lederer, Yiyuan She
We introduce and study the Group Square-Root Lasso (GSRL) method for estimation in high dimensional sparse regression models with group structure. The new estimator minimizes the s…
A robust, adaptive M-estimator for pointwise estimation in heteroscedastic regression
Michaël Chichignoud, Johannes Lederer
We introduce a robust and fully adaptive method for pointwise estimation in heteroscedastic regression. We allow for noise and design distributions that are unknown and fulfill ver…
How Correlations Influence Lasso Prediction
Mohamed Hebiri, Johannes C. Lederer
We study how correlations in the design matrix influence Lasso prediction. First, we argue that the higher the correlations are, the smaller the optimal tuning parameter is. This i…
Bounds for Rademacher Processes via Chaining
Johannes Christof Lederer
We study Rademacher processes where the coefficients are functions evaluated at fixed, but arbitrary covariables. Specifically, we assume the function class under consideration to…