activity
20082026
most citedEntropy inference and the James-Stein estimator, with application to nonlinear gene association networks

376 citations · 487 across the 5 of their papers we have counts for

collaborators

6 papers

stat.ME2026

From Physics to Statistics: A Simple Route to Exponential Families via Maximum Entropy

Korbinian Strimmer

Exponential families form the backbone of modern statistics and machine learning, but textbooks seldom derive them from first principles in an accessible way. Although minimal suff…

stat.ME2018

A whitening approach to probabilistic canonical correlation analysis for omics data integration

Takoua Jendoubi, Korbinian Strimmer

Background: Canonical correlation analysis (CCA) is a classic statistical tool for investigating complex multivariate data. Correspondingly, it has found many diverse applications,…

q-bio.QM2016

Mass spectrometry analysis using MALDIquant

Sebastian Gibb, Korbinian Strimmer

MALDIquant and associated R packages provide a versatile and completely free open-source platform for analyzing 2D mass spectrometry data as generated for instance by MALDI and SEL…

stat.ME2011★ 1 cited

Learning false discovery rates by fitting sigmoidal threshold functions

Bernd Klaus, Korbinian Strimmer

False discovery rates (FDR) are typically estimated from a mixture of a null and an alternative distribution. Here, we study a complementary approach proposed by Rice and Spiegelha…

stat.AP2009★ 110 cited

Gene ranking and biomarker discovery under correlation

Verena Zuber, Korbinian Strimmer

Biomarker discovery and gene ranking is a standard task in genomic high throughput analysis. Typically, the ordering of markers is based on a stabilized variant of the t-score, suc…

stat.ML2008★ 376 cited

Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

Jean Hausser, Korbinian Strimmer

We present a procedure for effective estimation of entropy and mutual information from small-sample data, and apply it to the problem of inferring high-dimensional gene association…