Publications

 
Dissemination of project results
Talks

Manuscripts

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

Contact

Lara Lusa
lara.lusa@mf.uni-lj.si
Institute for Biostatistics and Medical Informatics
Faculty of Medicine
University of Ljubljana
Vrazov trg 2, 1000 Ljubljana
Slovenia
 

Financed by :

 
Georg Heinze
georg.heinze@meduniwien.ac.at
Section for Clinical Biometrics
Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS)
Medical University of Vienna
Spitalgasse 23, 1090 Vienna
Austria

Financed by :