473 citations · 1.1k across the 8 of their papers we have counts for
8 papers
High-Dimensional Inference: Confidence Intervals, -Values and R-Software hdi
Ruben Dezeure, Peter Bühlmann, Lukas Meier +1
We present a (selective) review of recent frequentist high-dimensional inference methods for constructing -values and confidence intervals in linear and generalized linear model…
Discussion: "A significance test for the lasso"
Peter Bühlmann, Lukas Meier, Sara van de Geer
Discussion of "A significance test for the lasso" by Richard Lockhart, Jonathan Taylor, Ryan J. Tibshirani, Robert Tibshirani [arXiv:1301.7161].
GLMMLasso: An Algorithm for High-Dimensional Generalized Linear Mixed Models Using L1-Penalization
Jürg Schelldorfer, Lukas Meier, Peter Bühlmann
We propose an L1-penalized algorithm for fitting high-dimensional generalized linear mixed models. Generalized linear mixed models (GLMMs) can be viewed as an extension of generali…
P-values for high-dimensional regression
Nicolai Meinshausen, Lukas Meier, Peter Bühlmann
Assigning significance in high-dimensional regression is challenging. Most computationally efficient selection algorithms cannot guard against inclusion of noise variables. Asympto…
Discussion: One-step sparse estimates in nonconcave penalized likelihood models
Peter Bühlmann, Lukas Meier
Discussion of ``One-step sparse estimates in nonconcave penalized likelihood models'' [arXiv:0808.1012]
High-dimensional additive modeling
Lukas Meier, Sara van de Geer, Peter Bühlmann
We propose a new sparsity-smoothness penalty for high-dimensional generalized additive models. The combination of sparsity and smoothness is crucial for mathematical theory as well…