Indirect comparison of interaction graphs

Statistical Modelling and Regression Structures: Festschrift in Honour of Ludwig Fahrmeir

Volumn , Issue , 2010, Pages 249-265

  Mansmann, Ulrich ^a   Schmidberger, Markus ^a   Strobl, Ralf ^a   Jurinovic, Vindi ^a  

^a UNIVERSITY OF MUNICH   (Germany)

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EID: 79955786593     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-7908-2413-1_14     Document Type: Chapter

References (18)

1. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M. & Sherlock, G. (2000). Gene ontology:a tool for the unification of biology., Nature Genetics 25: 25-29.
2. Balasubramanian, R., LaFramboise, T., Scholtens, D. & Gentleman, R. (2004). A graph-theoretic approach to testing associations between disparate sources of functional genomics data., Bioin- formatics 20(18): 3353-3362.
3. Banerjee, O., Ghaoui, L. E. & d'Aspremont, A. (2008). Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data, Journal of Machine Learning Research pp. 485-516.
4. Friedman, J., Hastie, T. & Tibshirani, R. (2007). Sparse inverse covariance estimation with the graphical lasso, Biostatistics.
5. Gneiting, T. (2008). Editorial: Probabilistic forecasting, Journal of the Royal Statistical Society: Series A 17: 319-321.
6. Goeman, J. (2008). penalized: L1 (lasso) and L2 (ridge) penalized estimation in GLMs and in the Cox model. http://www.msbi.nl/goeman. R package version 0.9-22.
7. Goeman, J. (2009a). L1 and l2 penalized regression models. http://www.msbi.nl/goeman. R package version 0.9-24.
8. Goeman, J. (2009b). L1 penalized estimation in the cox proportional hazards model., Biometrical Journal.
9. Goeman, J. J. & Mansmann, U. (2008). Multiple testing on the directed acyclic graph of gene ontology., Bioinformatics 24(4): 537-544.
10. Kalisch, M. & Bühlmann, P. (2007). Estimating high dimensional acyclic graphs with the pc-algorithm, Journal of Machine Learning Research 8: 613-636.
11. Meinshausen, N. (2008). Hierarchical testing of variable importance, Biometrika 95: 265-276.
12. Meinshausen, N. & Bühlmann, P. (2006). High dimensional graphs and variable selection with the Lasso, The Annals of Statistics 34: 1436-1462.
13. R Development Core Team (2009). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org.
14. Ruschhaupt, M. (2008). Erzeugung von positiv definiten Matrizen mit Nebenbedingungen zur Validierung von Netzwerkalgorithmen für Microarray-Daten, PhD thesis, LMU München, Fakultät für Mathematik, Informatik und Statistik, München, Germany. http://edoc. ub.uni-muenchen.de/view/subjects/fak16.html.
15. Schäfer, J. & Strimmer, K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics., Statistical Applications in Genetics and Molecular Biology 4: Article32.
16. Schmidberger, M., Morgan, M., Eddelbuettel, D., Yu, H., Tierney, L. & Mansmann, U. (2009). State of the art in parallel computing with R, Journal of Statistical Software 31(1). http://www.jstatsoft.org/v31/i01/.
17. Wainwright, M. J., Ravikumar, P. & Lafferty, J. D. (2006). High dimensional graphical model selection using l1-regularized logistic regression, Proceedings of Advances in neural information processing systems 9: 1465-1472.
18. WHO (2001). International classification of functioning, disability and health (icf), Nature Genetics.