Armin Rauschenberger


PhD student working on high-dimensional statistics. I have developed methods for predicting a response from paired covariates, detecting interactive effects of binary covariates, and testing for global association with a count response. These methods are useful for analysing RNA sequencing data from cancer and normal samples.


palasso: paired lasso; sparse regression with paired covariates.

semisup: semi-supervised mixture model; detecting SNPs with interactive effect on a quantitative trait.

globalSeq: global test for counts; testing for association between RNA-Seq and high-dimensional data.


Rauschenberger A, Menezes RX, Jonker MA, and van de Wiel MA(2018). "Sparse regression with paired covariates." In preparation.

Rauschenberger A, Menezes RX, van de Wiel MA, van Schoor NM, and Jonker MA (2018). "Detecting SNPs with interactive effects on a quantitative trait." Submitted. link

Rauschenberger A, Jonker MA, van de Wiel MA, and Menezes RX (2016). "Testing for association between RNA-Seq and high-dimensional data." BMC Bioinformatics, 17:118. link