Publications
Dissemination of project results
Talks

New modifications of Firth's penalized logistic regression
by Angelika Geroldinger, at the Seminar in Graz on November 24th 2016, Austria 
Who‘s afraid of… Bayesian non‐collapsibility?
by Georg Heinze, at the Applied Statistics conference 2016, September 18  21, Ribno (Bled), Slovenia 
Rare events bias of logistic regression
by Rok Blagus, at the Applied Statistics conference 2016, September 18  21, Ribno (Bled), Slovenia and Annual Conference of the International Society for Clinical Biostatistics 2016, August 21  25, Birmingham, UK 
Firth's penalized likelihood logistic regression: accurate effect estimates AND predictions?
by Angelika Geroldinger, at the Fourth Joint Statistical Meeting of the Deutsche Arbeitsgemeinschaft Statistik 2016, March 1418, Göttingen, Germany 
Penalized likelihood logistic regression with rare events
by Georg Heinze, Invited Seminar in November 2015, ISPED, University of Bordeaux, France 
Penalized logistic regression with rare events: preliminary results
by Lara Lusa, at the Applied Statistics conference 2015, September 20  23, Ribno (Bled), Slovenia 
Accurate Prediction of Rare Events with Firth’s Penalized Likelihood Approach
by Angelika Geroldinger, at the Applied Statistics conference 2015, September 20  23, Ribno (Bled), Slovenia 
Challenges in accurate prediction of rare events with penalized likelihood methods
by Georg Heinze, at the Applied Statistics conference 2015, September 20  23, Ribno (Bled), Slovenia
Manuscripts

Separation in Logistic Regression  Causes, Consequences and Control
Mohammad Ali Mansournia, Angelika Geroldinger, Sander Greenland and Georg Heinze 
Firth’s logistic regression with rare events: accurate effect estimates AND predictions?
by Rainer Puhr, Georg Heinze, Marina Nold, Lara Lusa and Angelika Geroldinger 
What (not) to expect when classifying rare events
by Rok Blagus and Jelle J. Goeman 
Gradient boosting for highdimensional prediction of rare events
by Rok Blagus and Lara Lusa
Selected previous publications of members of our groups
 Blagus R and Lusa L. Class prediction for high‐dimensional class‐imbalanced data. BMC Bioinformatics 2010; 11:523.
 Blagus R and Lusa L. Improved shrunken centroid classifiers for high‐dimensional class‐imbalanced data. BMC Bioinformatics 2013; 14:64.
 Heinze G and Schemper M. A solution to the problem of monotone likelihood in Cox regression. Biometrics 2001; 57(1): 114‐119.
 Heinze G and Schemper M. A solution to the problem of separation in logistic regression. Statistics in Medicine 2002; 21(16): 2409‐2419.
 Heinze G. A comparative investigation of methods for logistic regression with separated or nearly separated data. Statistics in Medicine 2006; 25(24): 4216‐4226.
 Heinze G and Dunkler D. Avoiding infinite estimates of time‐dependent effects in small‐sample survival studies. Statistics in Medicine 2008; 27(30): 6455‐6469.
 Heinze G and Puhr R. Bias‐reduced and separation‐proof conditional logistic regression with small or sparse data sets. Statistics in Medicine 2010; 29(7‐8): 770‐777.
Background literature
 Ambler G, Seaman S and Omar RZ. An evaluation of penalised survival methods for developing prognostic models with rare events. Statistics in Medicine 2012; 31(11‐12): 1150‐1161.
 Bach FR. Bolasso: model consistent lasso estimation through the bootstrap. Proceedings of the 25th International Conference on Machine Learning, ACM 2008; 307: 33‐40.
 Bull SB, Mak C and Greenwood CMT. A modified score function estimator for multinomial logistic regression in small samples. Computational Statistics & Data Analysis 2002; 39(1): 57‐74.
 Bull SB, Lewinger JP and Lee SSF. Confidence intervals for multinomial logistic regression in sparse data. Statistics in Medicine 2007; 26(4): 903‐918.
 Firth D. Bias reduction of maximum likelihood estimates. Biometrika 1993; 80(1): 27‐38.
 Friedman J, Hastie T and Tibshirani R. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 2010; 33(1): 1‐22.
 Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. Biometrical Journal 2010; 52(1): 70‐84.
 He H and Garcia EA. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering 2009; 21(9): 1263–1284.
 Henderson R, Jones M and Stare J. Accuracy of point predictors in survival analysis. Statistics in Medicine 2001; 20(20):3083‐3096.
 Hoerl AE and Kennard RW. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970; 12(1):55‐67.
 Jeffreys H. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 1946; 186(1007): 453‐461.
 King G and Zeng L. Logistic regression in rare events data. Political analysis 2001; 9(2): 137‐163.25
 Lee SI, Lee H, Abbeel P and Ng AY. Efficient L1 regularized logistic regression. Proceedings of the 21st National Conference on Artificial Intelligence, AAAI 2006; 1: 401‐408.
 Lin IF, Chang WP and Liao YN. Shrinkage methods enhanced the accuracy of parameter estimation using Cox models with small number of events. Journal of Clinical Epidemiology 2013; 66(7): 743‐751.
 Meinshausen N and Bühlmann P. Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2010; 72(4): 417‐473.
 Shen J and Gao S. A solution to separation and multicollinearity in multiple logistic regression. Journal of Data Science 2008; 6(4): 515‐531.
 Simon N, Friedman J, Hastie T and Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. Journal of Statistical Software 2011; 39(5): 1‐13.
 Tibshirani R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 1996; 58(1): 267‐288.
 Verweij PJM and Van Houwelingen JC. Cross‐validation in survival analysis. Statistics in Medicine 1993; 12(24): 2305‐2314.
 Zou H and Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B(Statistical Methodology) 2005; 67(2): 301‐320.
 Zou H. The adaptive lasso and its oracle properties. Journal of the American Statistical Association 2006; 101(476): 1418‐1429