Publikationen

2023

Müller, C.M. & Schorning, K. (2023).  A-optimal designs for A-optimal designs for state estimation in networks. Statistical Papers 94. https://doi.org/10.1007/s00362-023-01435-y

Schürmeyer, L., Schorning, K. & Jörg Rahnenführer (2023). Design for the simultaneous inference of concentration-response curves. BMC Bioinformatics 24, 393.https://doi.org/10.1186/s12859-023-05526-3

Kappenberg, F., Duda, J. C., Schürmeyer, L., Gül, O., Brecklinghaus, T., Hengstler, J. G., Schorning, K., Rahnenführer, J. (2023). Guidance for statistical design and analysis of toxicological dose–response experiments based on a comprehensive literature review, Archives of Toxicology, 1-21. https://doi.org/10.1007/s00204-023-03561-w

2022

Möllenhoff, K., Schorning, K. & Kappenberg, F. (2022). Identifying alert concentrations using a model-based bootstrap approach. Biometrics, 00, 1– 13. https://doi.org/10.1111/biom.13799

2021

Kappenberg, F., Rahnenführer, J., & Schorning, K. (2021). Statistical approaches for calculating alert concentrations from cytotoxicity and gene expression data. Universitätsbibliothek Dortmund, Dortmund.

Schorning, K., & Dette, H. (2021). Optimal designs for comparing regression curves: dependence within and between groups. Journal of statistical theory and practice, 15(4). https://doi.org/10.1007/s42519-021-00218-8

Alhorn, K., Dette, H. & Schorning, K. (2021). Optimal Designs for Model Averaging in non-nested Models. Sankhya A 83, 745–778. https://doi.org/10.1007/s13171-020-00238-9

Schorning, K., Konstantinou, M. & Dette, H. (2021).  Optimal Designs for Series Estimation in Nonparametiric Regression with Correlated Data. Statistica Sinica 31, 1-25. doi:https://doi.org/10.5705/ss.202018.0497

2019

Alhorn, K., Schorning, K., & Dette, H. (2019). Optimal designs for frequentist model averaging. Biometrika, 106(3), 665–682. https://doi.org/10.1093/biomet/asz036

Dette, H., Konstantinou, M., Schorning, K., & Gösmann, J. (2019). Optimal designs for regression with spherical data. Electronic journal of statistics, 13(1), 361–390. https://doi.org/10.1214/18-ejs1524

Dette, H., & Schorning, K. (2019). Medikamentenstudien: Mit Statistik zur optimalen Dosis. In W. Krämer & C. Weihs (Hrsg.), Faszination Statistik (1. ed., S. 19–24). Berlin [u.a.]: Springer-Verlag. https://doi.org/10.1007/978-3-662-60562-2_3

2018

Schorning, K., Dette, H., Kettelhake, K., & Möller, T. (2018). Optimal designs for non-competitive enzyme inhibition kinetic models. Statistics, 52(6), 1359–1378. https://doi.org/10.1080/02331888.2018.1511716

2017

Dette, H., Hoyden, L., Kuhnt, S., & Schorning, K. (2017). Optimal designs for thermal spraying. Journal of the Royal Statistical Society / Royal Statistical Society / C Series C, Applied statistics, 66(1), 53–72. https://doi.org/10.1111/rssc.12156

Dette, H., Schorning, K., & Konstantinou, M. (2017). Optimal designs for comparing regression models with correlated observations. Computational statistics & data analysis, 113, 273–286. https://doi.org/10.1016/j.csda.2016.06.017

Feller, C., Schorning, K., Dette, H., Bermann, G., & Bornkamp, B. (2017). Optimal designs for dose response curves with common parameters. The annals of statistics, 45(5), 2102–2132. https://doi.org/10.1214/16-aos1520

Schorning, K., Dette, H., Kettelhake, K., Wong, W. K., & Bretz, F. (2017). Optimal designs for active controlled dose-finding trials with efficacy-toxicity outcomes. Biometrika, 104(4), 1003–1010. https://doi.org/10.1093/biomet/asx057

2016

Dette, H., & Schorning, K. (2016). Optimal designs for comparing curves. The annals of statistics, 44(3), 1103–1130. https://projecteuclid.org/euclid.aos/1460381688

Schorning, K., Bornkamp, B., Bretz, F., & Dette, H. (2016). Model selection versus model averaging in dose finding studies. Statistics in medicine, 35(22), 4021–4040. https://doi.org/10.1002/sim.6991

2013

Dette, H., & Schorning, K. (2013). Complete classes of designs for nonlinear regression models and principal representations of moment spaces. The annals of statistics, 41(3), 1260–1267. https://doi.org/10.1214/13-aos1108