Publikationen

• Grömping, U. (2019). The Sleeping Beauty Problem Demystified. Report 2/2019, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.

• Grömping, U. (2017-2019). DoE.MIParray: Creation of Arrays by Mixed Integer Programming. R package version 0.12. In: R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. and Fontana, R. (2019). An Algorithm for Generating Good Mixed Level Factorial Designs. Computational Statistics & Data Analysis 137, 101-114. doi:10.1016/j.csda.2019.01.020. Download paper (free download for limited time only). Embargo period for green open access: 24 months.
  The technical report Grömping and Fontana (2018) is the preprint of this article before peer review (peer review heavily modified (and improved) it).
  

• Grömping, U. (2018). A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R. Journal of Statistical Software 86, book review 3, 1-4.

• Grömping, U. (2009-2018). The DoE.base Package (Full factorials, orthogonal arrays and base utilities for DoE packages). R package version 1.1-1. In: R Development Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. (2018). R Package DoE.base for Factorial Designs. Journal of Statistical Software 85(5), 1-41.

• Grömping, U. (2018). prepplot: Prepare Figure Region for Base Graphics. R package version 0.7 (Manual, Vignette). In: R Development Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. (2008-2018). The ic.infer Package (Inequality Constrained Inference in Linear Normal Situations). R package version 1.1-6. In: R Development Core Team (2018). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. and Fontana, R. (2018). An Algorithm for Generating Good Mixed Level Factorial Designs. Report 1/2018, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin. (a modified and improved version of this preprint is available as Grömping and Fontana 2019)

• Grömping, U. (2018). Coding Invariance in Factorial Linear Models and a New Tool for Assessing Combinatorial Equivalence of Factorial Designs. Journal of Statistical Planning and Inference 193, 1-14. https://doi.org/10.1016/j.jspi.2017.07.004. Embargo period for green open access: 24 months. The technical report Grömping (2017) is the preprint of this article before peer review.

• Grömping, U. (2017). Data visualisation with R: 100 examples. Journal of Statistical Software 81, book review 2, 1-5.

• Grömping, U. (2009-2017). The DoE.wrapper Package (Wrapper package for design of experiments functionality). R package version 0.9. In: R Core Team (2017). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. (2017). Frequency tables for the coding invariant quality assessment of factorial designs. IISE Transactions 49, 505-517. http://www.tandfonline.com/doi/full/10.1080/0740817X.2016.1241458; download Accepted Manuscript, download free eprint of printed version.
  

• Grömping, U. (2017). An Interaction-Based Decomposition of Generalized Word Counts Suited to Assessing Combinatorial Equivalence of Factorial Designs. Report 1/2017, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin. Further Examples.

• Vasilev, N., Boccard, J., Lang, G., Grömping, U., Fischer, R., Goepfert, S., Rudaz, S. and Schillberg, S. (2016). Structured plant metabolomics for the simultaneous exploration of multiple factors. Scientific Reports 6, Article number: 37390 (15 pages). doi:10.1038/srep37390. http://www.nature.com/articles/srep37390.

• Grömping, U. (2016). Interaction Contributions as Coding Invariant Single Degree of Freedom Contributions to Generalized Word Counts. Report 2/2016, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin. (Report 1/2017 is an extended and refocussed version of this report; if you are not looking for something very specific from this report, I recommend to consult that new report.)

• Grömping, U. (2007-2016). The FrF2 Package (Fractional Factorial designs with 2-level factors). R package version 1.7-2. In: R Development Core Team (2016). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. (2016). Practical Guide to Logistic Regression. Journal of Statistical Software 71, book review 3, 1-5.

• Grömping, U. (2016). Corrigendum to Variable importance in regression models. WIREs Comput Stat 8(4), 154-157. (link to original article with corrected supporting information) 

• Grömping, U. and Bailey, R.A. (2016). Regular fractions of Factorial Arrays. In: Kunert, J., Müller, C. and Atkinson, A. (eds.), mODa 11 – Advances in Model-Oriented Design and Analysis, Springer International Publishing Switzerland, 143-151. Accepted Author Manuscript.

• Grömping, U. (2016). R Package DoE.base for Factorial Designs. Report 1/2016, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.

• Grömping, U. (2015). Using R and RStudio for Data Management, Statistical Analysis and Graphics (2nd Edition). Journal of Statistical Software 68, book review 4, 1-7.

• Grömping, U. (2015). Augmented Half Normal Effects Plots in the Presence of a Few Error Degrees of Freedom. Quality and Reliability Engineering International 31(7), 1185-1196. Supplementary material. Code for creating the figures in the paper

• Grömping, U. (2015). Variable importance in regression models. WIREs Comput Stat 7, 137-152. DOI: 10.1002/wics.1346. (Corrigendum, regarding the Fabbris metric and numeric results on the Green metric)
  

• Grömping, U. (2015). Multilevel Modeling Using R. Journal of Statistical Software 62, book review 1, 1-6.

• Vasilev, N., Schmitz, Ch., Grömping, U., Fischer, R. and Schillberg, S. (2014). Assessment of Cultivation Factors that Affect Biomass and Geraniol Production in Transgenic Tobacco Cell Suspension Cultures. PLoS ONE 9(8): e104620 (7 pages). DOI:10.1371/journal.pone.0104620.

• Grömping, U. and Xu, H. (2014). Generalized resolution for orthogonal arrays. The Annals of Statistics 42(3), 918-939.

• Grömping, U. (2014). Mosaic plots are useful for visualizing low order projections of factorial designs. The American Statistician 68(2), 108-116. DOI: 10.1080/00031305.2014.896829.
  Download: accepted author version, supplementary material

• Grömping, U. (2014). A Note on Dominating Fractional Factorial Two-Level Designs With Clear Two-Factor Interactions. Technometrics 56, 42-45. DOI: 10.1080/00401706.2013.822425.
  Download: accepted author version, supplementary material

• Grömping, U. (2014). R Package FrF2 for Creating and Analysing
  Fractional Factorial 2-Level Designs. Journal of Statistical Software 56 (1), 1-56.

• Grömping, U. (2013). Frequency tables for the coding invariant ranking of orthogonal arrays. Report 2/2013, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.

• Grömping, U. and Xu, H. (2013). Generalized resolution for orthogonal arrays. Report 1/2013, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.

• Vasilev, N., Grömping, U., Lipperts, A., Raven, N., Fischer, R. and Schillberg, S. (2013). Optimization of BY-2 cell suspension culture medium for the production of a human antibody using a combination of fractional factorial designs and the response surface method. Plant Biotechnology Journal 11(7), 867-874.

• Grömping, U. (2009-2013). The RcmdrPlugin.DoE Package (R Commander Plugin for (industrial) Design of Experiments). R package version 0.12-2. In: R Development Core Team (2013). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. (2013). The R Primer.  Journal of Statistical Software 52, book review 3, 1-5.

• Grömping, U. (2012). Variable Importance Assessment in Regression: Linear Regression Versus the Random Forest. In:Vogt, W. P. and Johnson, R. B. (eds.) SAGE Benchmarks in Social Research Methods: Correlation and Regression Analysis (Vol I). Sage, Los Angeles.
  (Reprint of Grömping 2009 American Statistician)

• Grömping, U. (2012). Creating clear designs: a graph-based algorithm and a catalog of clear compromise plans. IIE Transactions 44, 988-1001. DOI:10.1080/0740817X.2012.654848.
  Download: accepted author version, free eprint of printed version, supplementary material.

• Grömping, U. (2010-2013). The FrF2.catlg128 Package (Catalogues of resolution IV 128 run 2-level fractional factorials up to 33 factors that do have 5-letter words). R package version 1.2-1. In: R Development Core Team (2012). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.

• Grömping, U. (2011). Tutorial for designing experiments using the R package RcmdrPlugin.DoE. Report 4/2011, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.
  Example data can be found here.

• Grömping, U. (2011). Optimal Design of Experiments with R.  Journal of Statistical Software 43, book review 5, 1-4.

• Grömping, U. (2011). Industrial Design of Experiments in R. In: Melas, V., Nachtmann, G. and Rasch, D.: Optimal Design of Experiments -
  Theory and Application: Proceedings of the International Conference
  in Honor of the late Jagdish Srivastava. Center of Experimental Design
  University of Natural Resources and Life Sciences, Vienna. ISBN 978-3-900962-96-8. pp 42-58.

• Grömping, U. (2011). Relative projection frequency tables for orthogonal arrays. Report 1/2011, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.

• Grömping, U. (2010). Design and Analysis of Experiments with SAS.  Journal of Statistical Software 37, book review 2, 1-3.

• Grömping, U. (2010). Creating clear designs: a graph-based algorithm and a catalogue of clear compromise plans. Report 5/2010, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin. (preprint of 2012 IIE Transactions article)

• Grömping, U. (2010). "Clear" and "Distinct": two approaches for regular fractional factorial designs with estimability requirements. Report 2/2010, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.

• Grömping, U. and Landau, S. (2010). Do not adjust coefficients in Shapley value regression. Applied Stochastic Models in Business and Industry 26, 194-202. Abstract: DOI: 10.1002/asmb.773. Final author version: PDF.

• Grömping, U. (2010). Inference With Linear Equality And Inequality Constraints Using R: The Package ic.infer. Journal of Statistical Software 33, Issue 10, 1-31.

• Grömping, U. (2009). Variable Importance Assessment in Regression: Linear Regression Versus Random Forest. The American Statistician 63, 308-319.
  With supplemental files programs.zip and Supplement_shape.pdf.
  Furthermore, on reader request, with a link to the Swiss dataset and a program for reading and preparing it.

• Hautzinger, H., Grömping,  U., Kreiß, J.-P., Mougeot, M., Pastor, C., Pfeiffer, M. and Zangmeister, T. (2008). Summary Report of Work Package 7 - Statistical Methods, Deliverable 7.5 for EU-Project TRaffic Accident Causation in Europe (Project No. 027763 – TRACE).

• Grömping, U., Pfeiffer, M. and Stock, W. (2007). Statistical Methods for Improving the Usability of Existing Accident Databases. Deliverable 7.1 for EU-Project TRaffic Accident Causation in Europe (Project No. 027763 – TRACE).

• Grömping, U. (2007). Response to comment by Scott Menard, re: Estimators of Relative Importance in Linear Regression Based on Variance Decomposition. In: Letters to the Editor, The American Statistician 61, 280-284.

• Grömping, U. (2007). Estimators of Relative Importance in Linear Regression Based on Variance Decomposition. The American Statistician 61, 139-147.

• Grömping, U. (2006). Relative Importance for Linear Regression in R: The Package relaimpo. Journal of Statistical Software 17, Issue 1.

• Gerhorst, F., Grömping, U., Lloyd-Thomas, D., Khalaf, F. (2006): Design for Six Sigma in product development at Ford Motor Company in a case study on robust exhaust manifold design. Int. J. Product Development 3, 278-291.

• Grömping, U., Menzler, S. and Weimann, U. (2005):  Split-Register Study: A New Method for Estimating the Impact of Rare Exposure on Population Accident Risk based on Accident Register Data. In: BASt (2005, ed.): 1st International Conference on ESAR (Expert Symposium on Accident Research). Series "Berichte der Bundesanstalt für Straßenwesen", sub series "Fahrzeugtechnik", Report F55, 95-101.

• Grömping, U. and Weimann, U. (2004). The asymptotic distribution of the partial attributable risk in cross-sectional studies. Statistics 38, 427-438.

• Heinrichs, R. und Grömping, U. (2004). Customer Driven Diesel Vehicle Sound Quality. Proceedings of Internoise, PRAG 2004.

• Heinrichs, R. und Grömping, U. (2003). Hauptdimension der Dieselgeräusch-Wahrnehmung. Proceedings of DAGA, Aachen 2003.

• Grömping, U. and Weimann, U. (2003). A Statistical Method for Customer Satisfaction Inhibitor Estimation that Permits Calculation of Confidence Intervals. Ford Technical Journal, Vol. 6, No. 1 (January 2003, FTJ-2003-0007).

• Grömping, U. and Weimann, U. (2002, with revision in 2003). The asymptotic distribution of the partial attributable risk in cross-sectional studies. More elaborate preprint of 2004 paper. Available on the Mathematics preprint server (link works after you have logged into the Mathematics preprint server). Download from here without login.

• Grömping, U. and Bazant, C. (2002). Case Study "Seat Belt Retraction Time". Published on the ENBIS web site http://www.enbis.org/sig/design_of_experiments/docs/seat_belt_retraction_time (case studies accessible for ENBIS members only, membership free of charge).

• Grömping, U. (2001). One-sided Likelihood Ratio Tests for Linear Inequalities on the Parameters of Normal Linear Models. In: Kunert, J. and Trenkler, G. (eds.): Mathematical Statistics with Applications in Biometry: Festschrift in Honour of Siegfried Schach. Eul, Köln.

• Ziegler, A. und Grömping, U. (1998). The Generalised Estimating Equations: A Comparison of Procedures Available in Commercial Statistical Software Packages. Biometrical Journal 40, 245-260.

• Grömping, U. (1996). Tests for a monotone dose-response relation in models with ordered categorical dose with emphasis on likelihood ratio tests for linear inequalities on normal means. PhD Thesis, Department of Statistics, Dortmund University, Germany.

• Ziegler, A., Kastner, C., Grömping, U. und Blettner, M. (1996). The Generalized Estimating Equations in the Past Ten Years: An Overview and a Biomedical Application. Discussion Paper 24, Sonderforschungsbereich “Statistische Analyse diskreter Strukturen“ (SFB 386), Ludwig-Maximilians-University Munich, Germany.

• Vach, W., Grömping, U., Schulz, K. (1996). On the relation between parameter estimates from transition and marginal models for longitudinal data. Research Report  28 at Freiburger Zentrum für Datenanalyse und Modellbildung, Albert-Ludwigs-University Freiburg, Germany. Postscript-File.

• Grömping, U. (1996). A note on fitting a marginal model to mixed effects log-linear regression data via GEE. Biometrics 52, 280-285.

• Ziegler, A., Kastner, C., Grömping, U. und Blettner, M. (1996). The Generalized Estimating Equations: Derivation and Applications (in German). Informatik, Biometrie und Epidemiologie in Medizin und Biologie 27, 69-91.

• Barowski, M., Weimann, B., Grömping, U. und Schach, S. (1995). SAS. A course on the SAS-modules STAT, IML und GRAPH for advanced learners (in German). Script, Department of Statistics, Dortmund University, Germany.

• Luttmann, H., Grömping, U., Kreienbrock, L., Treiber-Klötzer, C. und Wichmann, H.E. (1995). Kohortenstudie zu Atemwegserkrankungen und Lungenfunktion bei Schulkindern in Südwestdeutschland, Teil 4: Innerstädtische Umwelteinflüsse auf Atemwegserkrankungen in Mannheim. Zentralblatt für Hygiene und Umweltmedizin 198, 172-189.

• Grömping, U. (1995). Subject-specific and population-averaged questions for log-linear regression data. In: Seeber, G.U.H. et al. (eds.): Statistical Modelling. Proceedings of the 10th International Workshop on Statistical Modelling, Innsbruck, Austria, 10-14 July, Springer, Heidelberg.

• Grömping, U. (1994). Simple one- and two-sided likelihood ratio tests for multivariate normal means with applications as confirmatory trend tests. Research report No. 94/5, Department of Statistics, Dortmund University, Germany.

• Grömping, U. (1993). GEE: A SAS Macro for Longitudinal Data Analysis (Version 2.0). Technical Report (Arbeitsbericht) 31, Department of Statistics, Dortmund University, Germany.

• Luttmann, H., Grömping, U., Treiber-Klötzer, C. Kreienbrock, L., und Wichmann, H.E. (1993). Kohortenstudie zu Atemwegserkrankungen und Lungenfunktion bei Schulkindern in Südwestdeutschland, Teil 2: Regionale Einflüsse auf Atemwegserkrankungen in Mannheim und im Raum Freiburg. Zentralblatt für Hygiene und Umweltmedizin 196, 114-138.

• Luttmann, H., Grömping, U., Kreienbrock, L., Treiber-Klötzer, C. und Wichmann, H.E. (1993). Kohortenstudie zu Atemwegserkrankungen und Lungenfunktion bei Schulkindern in Südwestdeutschland, Teil 1: Studiendesign, Prävalenz und Inzidenz von Atemwegserkrankungen. Zentralblatt für Hygiene und Umweltmedizin 194, 525-539.

• Luttmann, H., Grömping, U., Wichmann, H.E., Kreienbrock, L. und Treiber-Klötzer, C. (1992). Auswirkungen der Luftverunreinigung auf die Gesundheit von Schulkindern. In: Van Eimeren, W., Überla, K., Ulm, K. (Hrsg.). Gesundheit und Umwelt. Medizinische Informatik, Biometrie und Epidemiologie 75, Springer, Berlin, 131-146.

• Grömping, U. (1990). The EM Algorithm for Maximum-Likelihood-estimation in mixed linear models (in German). Diploma thesis, Department of Statistics, Dortmund University, Germany.

• Grömping, U., Hennig, M., Kübler, J. und Schach, S. (1990). Introduction to the Statistical Analysis System (in German). Script, Department of Statistics, Dortmund University, Germany.