Peer-reviewed journal articles – first and last authorships

Selected publications:

• Behning, C., A. Bigerl, M. N. Wright, P. Sekula, M. Berger and M. Schmid (2024): Random survival forests with competing events: A subdistribution-based imputation approach. Biometrical Journal 66 (6): e202400014.
• Schenk, A., M. Berger and M. Schmid (2024): Pseudo-value regression trees. Lifetime Data Analysis 30 (2), 439-471.
• Berger, M., A. Kowark, R. Rossaint, M. Coburn and M. Schmid (2023): Modeling postoperative mortality in older patients by boosting discrete-time competing risks models. Journal of the American Statistical Association 118 (544), 2239-2249.
• Schmid, M., T. Friede, N. Klein and L. Weinhold (2023): Accounting for time dependency in meta-analyses of concordance probability estimates. Research Synthesis Methods 14 (6), 807-823.
• Maloney, K. O., C. Buchanan, R. D. Jepsen, K. P. Krause, M. J. Cashman, B. P. Gressler, J. A. Young and M. Schmid (2022): Explainable machine learning improves interpretability in the predictive modeling of biological stream conditions in the Chesapeake Bay Watershed, USA. Journal of Environmental Management 322:116068.
• Kristiansen, G. and M. Schmid (2022): Application of computer-generated images to train pattern recognition used in semiquantitative immunohistochemistry scoring. Journal of Pathology, Microbiology and Immunology – the APMIS journal 130 (1), 26-33.
• Welchowski, T., K. O. Maloney, R. Mitchell and M. Schmid (2022): Techniques to improve ecological interpretability of black-box machine learning models: Case study on biological health of streams in the United States with gradient boosted trees. Journal of Agricultural, Biological and Environmental Statistics 27 (1), 175-197.
• Kowark, A., M. Berger, R. Rossaint, M. Schmid* and M. Coburn* (2022): Association between benzodiazepine premedication and 30-day mortality rate: A propensity-score weighted analysis of the Peri-interventional Outcome Study in the Elderly (POSE). European Journal of Anaesthesiology 39 (3), 210-218. *Contributed equally to the contents of the manuscript.
• Berger, M. and M. Schmid (2022): Assessing the calibration of subdistribution hazard models in discrete time. The Canadian Journal of Statistics 50 (2), 572-591.
• Behning, C., M. Fleckenstein, M. Pfau, C. Adrion, L. Goerdt, M. Lindner, S. Schmitz-Valckenberg, F. G. Holz and M. Schmid (2021): Modeling of atrophy size trajectories: variable transformation, prediction and age-of-onset estimation. BMC Medical Research Methodology 21:170.
• Vogt, M. and M. Schmid (2021): Clustering with statistical error control. Scandinavian Journal of Statistics 48 (3), 729-760.
• Schmid, M. and M. Berger (2021): Competing risks analysis for discrete time-to-event data. WIREs Computational Statistics 13 (5): e1529.
• Gorgi Zadeh*, S. and M. Schmid* (2021): Bias in cross-entropy-based training of deep survival networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 43 (9), 3126-3137. *Contributed equally to the contents of the manuscript.
• Berger, M. and M. Schmid (2020): Flexible modeling of ratio outcomes in clinical and epidemiological research. Statistical Methods in Medical Research 29 (8), 2250-2268.
• Berger*, M., M. Schmid*, T. Welchowski, S. Schmitz-Valckenberg and J. Beyersmann (2020): Subdistribution hazard models for competing risks in discrete time. Biostatistics 21 (3), 449-466. *Contributed equally to the contents of the manuscript.
• Schmid, M., T. Welchowski, M. N. Wright and M. Berger (2020): Discrete-time survival forests with Hellinger distance decision trees. Data Mining and Knowledge Discovery 34 (3), 812-832.
• Welchowski, T., V. Zuber and M. Schmid (2019): Correlation-adjusted regression survival scores for high-dimensional variable selection. Statistics in Medicine 38 (13), 2413-2427.
• Berger, M., M. Wagner and M. Schmid (2019): Modeling biomarker ratios with gamma distributed components. The Annals of Applied Statistics 13 (1), 548-572.
• Perperoglou, A., W. Sauerbrei, M. Abrahamowicz and M. Schmid (2019): A review of spline function procedures in R. BMC Medical Research Methodology 19:46.
• Berger, M., G. Tutz and M. Schmid (2019): Tree-structured modelling of varying coefficients. Statistics and Computing 29 (2), 217-229.
• Mayr, A., L. Weinhold, B. Hofner, S. Titze, O. Gefeller and M. Schmid (2018): The betaboost package – a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data. International Journal of Epidemiology 47 (5), 1383-1388.
• Schmid, M., G. Tutz and T. Welchowski (2018): Discrimination measures for discrete time-to-event predictions. Econometrics and Statistics 7, 153-164.
• Berger, M. and M. Schmid (2018): Semiparametric regression for discrete time-to-event data. Statistical Modelling 18 (3-4), 322-345.
• Lindner, M, J. Nadal, M. M. Mauschitz, A. Lüning, J. Czauderna, M. Pfau, S. Schmitz-Valckenberg, F. G. Holz, M. Schmid* and M. Fleckenstein* (2017). Combined fundus autofluorescence and near infrared reflectance as prognostic biomarkers for visual acuity in foveal-sparing geographic atrophy. Investigative Ophthalmology & Visual Science 58 (6), BIO61-BIO67. *Contributed equally to the contents of the manuscript.
• Weinhold, L., S. Wahl, S. Pechlivanis, P. Hoffmann and M. Schmid (2016): A statistical model for the analysis of beta values in DNA methylation studies. BMC Bioinformatics 17:480.
• Hofner, B., A. Mayr and M. Schmid (2016): gamboostLSS: An R package for model building and variable selection in the GAMLSS framework. Journal of Statistical Software 74 (1), 1-31.
• Schmid, M., M. N. Wright and A. Ziegler (2016): On the use of Harrell’s C for clinical risk prediction via random survival forests. Expert Systems with Applications 63, 450-459.
• Welchowski, T. and M. Schmid (2016): A framework for parameter estimation and model selection in kernel deep stacking networks. Artificial Intelligence in Medicine 70, 31-40.
• Casalicchio, G., B. Bischl, A.-L. Boulesteix and M. Schmid (2016): The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72 (2), 392-401.
• Schmid, M., H. Küchenhoff, A. Hoerauf and G. Tutz (2016): A survival tree method for the analysis of discrete event times in clinical and epidemiological studies. Statistics in Medicine 35 (5), 734-751.
• Schmid, M., H. A. Kestler and S. Potapov (2015): On the validity of time-dependent AUC estimators. Briefings in Bioinformatics 16 (1), 153-168.
• Mayr, A., H. Binder, O. Gefeller and M. Schmid (2014): The evolution of boosting algorithms – from machine learning to statistical modelling. Methods of Information in Medicine 53 (6), 419-427.
• Wahl, S., N. Fenske, S. Zeilinger, K. Suhre, C. Gieger, M. Waldenberger, H. Grallert and M. Schmid (2014): On the potential of models for location and scale for genome-wide DNA methylation data. BMC Bioinformatics 15:232.
• Hofner, B., A. Mayr, N. Robinzonov and M. Schmid (2014): Model-based boosting in R: A hands-on tutorial using the R package mboost. Computational Statistics 29 (1-2), 3-35.
• Mayr, A. and M. Schmid (2014): Boosting the concordance index for survival data – a unified framework to derive and evaluate biomarker combinations. PLoS ONE 9 (1): e84483.
• Schmid, M., F. Wickler, K. O. Maloney, R. Mitchell, N. Fenske and A. Mayr (2013): Boosted beta regression. PLoS ONE 8 (4): e61623.
• Schmid, M. and S. Potapov (2012): A comparison of estimators to evaluate the discriminatory power of time-to-event models. Statistics in Medicine 31 (23), 2588-2609.
• Mayr, A., N. Fenske, B. Hofner, T. Kneib and M. Schmid (2012): Generalized additive models for location, scale and shape for high dimensional data – a flexible approach based on boosting. Journal of the Royal Statistical Society, Series C 61 (3), 403-427.
• Maloney*, K. O., M. Schmid* and D. E. Weller (2012): Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages. Methods in Ecology and Evolution 3 (1), 116-128. *Contributed equally to the contents of the manuscript.
• Hofner, B., T. Hothorn, T. Kneib and M. Schmid (2011): A framework for unbiased model selection based on boosting. Journal of Computational and Graphical Statistics 20 (4), 956-971.
• Schmid, M., T. Hothorn, K. O. Maloney, D. E. Weller and S. Potapov (2011): Geoadditive regression modeling of stream biological condition. Environmental and Ecological Statistics 18 (4), 709-733.
• Schmid, M., T. Hielscher, T. Augustin and O. Gefeller (2011): A robust alternative to the Schemper-Henderson estimator of prediction error. Biometrics 67 (2), 524-535.
• Schmid, M., S. Potapov, A. Pfahlberg and T. Hothorn (2010): Estimation and regularization techniques for regression models with multidimensional prediction functions. Statistics and Computing 20 (2), 139-150.
• Schmid, M. (2009): The effect of single-axis sorting on the estimation of a linear regression. Journal of Official Statistics 25 (4), 529-548.
• Schmid, M. and H. Schneeweiss (2009): The effect of microaggregation by individual ranking on the estimation of moments. Journal of Econometrics 153 (2), 174-182.
• Schmid, M. and T. Hothorn (2008): Boosting additive models using component-wise P-splines. Computational Statistics & Data Analysis 53 (2), 298-311.
• Schmid, M. and T. Hothorn (2008): Flexible boosting of accelerated failure time models. BMC Bioinformatics 9:269.
• Schmid, M., H. Schneeweiss and H. Küchenhoff (2007): Estimation of a linear regression under microaggregation with the response variable as a sorting variable. Statistica Neerlandica 61 (4), 407-431.