About
I am an Assistant Professor in the Department of Statistics at George Mason University.
Prior to joining GMU, I obtained a PhD in Statistics in 2020 from the University of British Columbia where I was advised by Dr. Gabriela Cohen Freue. Before that, I obtained a Master of Science in Statistics from the Vienna University of Technology under supervision by Prof. Peter Filzmoser.
My research agenda comprises methodological and computational aspects of robust estimation in high-dimensional problems as well as their application to Biomedical Sciences. I am working on statistical methods with reliable performance under the presence of adverse contamination anywhere in the numerous features of the data.
For regression problems, for instance, I work on estimators which are resilient to outliers in the response but also to unusual values in the (potentially) explanatory variables. If not handled appropriately, unusual values in the explanatory variables can have a much more detrimental affect on the analysis than outliers in the response alone.
Teaching
George Mason University
- Spring 21: STAT 634 – Case Studies in Data Analysis
University of British Columbia
- Winter 2019/20 Term 2: STAT 305 – Introduction to Statistical Inference
Publications
2021
- Kepplinger D. Robust variable selection and estimation via adaptive elastic net S-estimators for linear regression. arXiv e-prints. arXiv:2107.03325
2019
- Cohen Freue GV, Kepplinger D*, Salibián-Barrera M, Smucler E. Robust elastic net estimators for variable selection and identification of proteomic biomarkers. Annals of Applied Statistics.2019;13(4). online pdf (* in alphabetical order)
2017
- Kepplinger D, Takhar M, Sasaki M, Hollander Z, Smith D, McManus B, et al. PGCA: An algorithm to link protein groups created from MS/MS data. PLOS ONE. 2017;12(5). online
- Kepplinger D, Filzmoser P, Varmuza K. Variable selection with genetic algorithms using repeated cross- validation of PLS regression models as fitness measure. preprint
2013
- Kepplinger D, Templ M, Upadhyaya S. Analysis of energy intensity in manufacturing industry using mixed-effects models. Energy. 2013;59:754 – 763. online
A complete list of publications, conference presentations, and other research experience can be found in my CV.
Code
I am maintaining several stable R packages on CRAN and Bioconductor as well as a few experimental software tools available on my GitHub and GitLab pages.
examinr
Create online exams from R markdown documents.
Write online exams as R markdown documents and publish them as shiny app. Allows for randomized exams, different question types (including R coding questions), and grading of submissions.
More info
pense
Implementation of penalized adaptive Elastic Net S/MM-Estimators of Regression
Robust penalized adaptive elastic net S- and MM-estimators for linear regression.
More info View on CRAN
pyinit
Peña-Yohai Initial Estimator for Robust S-Regression
Fast and deterministic procedure to compute initial estimates for robust S-estimators of regression using as described in Peña-Yohai (1999).
View on Github View on CRAN
gaselect
Genetic algorithms for variable selection.
Multi-threaded genetic algorithms applicable to a wide range of variable selection methods, but particularly suited for Partial Least Squared Regression.
View on CRAN
complmrob
Robust Linear Regression with Compositional Covariates
Methods for robustly fitting regression models where the explanatory variables are compositional. Includes bootstrap methods for classical robust regression and compositional robust regression.
View on CRAN
nsoptim
Algorithms for non-smooth optimization
C++ template library, wrapped in an R package, providing modern and fast algorithms for optimizing non-smooth functions (e.g., L1 regularized objective functions).
View on GitLab
PGCA
Link Protein Groups Created from MS/MS Data
Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data.
View on Bioconductor