Marloes Maathuis

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(most of these papers are on Google scholar)

Working papers:

Publications:

  1. S. Ravizza, J.A.D. Atkin, M.H. Maathuis and E.K. Burke (2013). A combined statistical approach and ground movement model for improving taxi time estimations at airports. Journal of the Operational Research Society, advance online publication, 3 October 2012. (DOI 10.1057/jors.2012.123)
  2. D.J. Stekhoven, L. Hennig, G. Sveinbjörnsson, I. Moraes, M.H. Maathuis, P. Bühlmann (2012). Causal stability ranking. Bioinformatics 28 2819-2823. (published version)
  3. M. Kalisch, M. Mächler, D. Colombo, M.H. Maathuis and P. Bühlmann (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software, Vol 47, Issue 11, 1-26. (published version)
  4. D. Colombo, M.H. Maathuis, M. Kalisch and T.S. Richardson (2012). Learning high-dimensional directed acyclic graphs with latent and selection variables. Annals of Statistics 40 294-321. (arXiv:1104.5617v2, published version, extended abstract for UAI2011)
  5. T. Gsponer, M. Petersen, M. Egger, S. Phiri, M.H. Maathuis, A. Boulle, H. Tweyad, K. Peter, B.H. Chi and O. Keiser (2012). The causal effect of switching to second-line ART in programmes without access to routine viral load monitoring. AIDS 26, 57-65. (published version)
  6. N. Meinshausen, M.H. Maathuis and P. Bühlmann (2011). Optimality of the Westfall-Young permutation procedure for multiple testing under dependence. Annals of Statistics 39, 3369-3391. (arXiv:1106.2068v2, published version)
  7. M.H. Maathuis and M.G. Hudgens (2011). Nonparametric inference for competing risks current status data with continuous, discrete or grouped observation times. Biometrika 98, 325-340. (arxiv:0909.4856v3, published version)
  8. P. Bühlmann, M. Kalisch and M.H. Maathuis (2010). Variable selection in high-dimensional models: partially faithful distributions and the PC-simple algorithm. Biometrika 97, 261-278. (arXiv:0906.3204v3, published version)
  9. M.H. Maathuis, D. Colombo, M. Kalisch and P. Bühlmann (2010). Predicting causal effects in large-scale systems from observational data. Nature Methods 7, 247-248. (published version, supplementary information) (See also the editorial on cause and effect in the same issue)
  10. M. Kalisch, B. Fellinghauer, E. Grill, M.H. Maathuis, U. Mansmann, P. Bühlmann and G. Stucki (2010). Understanding human functioning using graphical models. BMC Medical Research Methodology 10:14. (open source published version)
  11. A.J. Caesar, T. Caesar and M.H. Maathuis (2010). Pathogenicity, characterization and comparative virulence of Rhizoctonia spp. from insect-galled roots of Lepidium draba in Europe. Biological Control 52, 140-144. (preprint, published version)
  12. M.H. Maathuis, M. Kalisch, P. Bühlmann (2009). Estimating high-dimensional intervention effects from observational data. Annals of Statistics 37, 3133-3164. (arXiv:0810.4214v3, published version)
  13. P. Groeneboom, M.H. Maathuis and J.A. Wellner (2008). Current status data with competing risks: limiting distribution of the MLE. Annals of Statistics 36, 1064-1089. (arXiv:math/0609021v2, published version)
  14. P. Groeneboom, M.H. Maathuis and J.A. Wellner (2008). Current status data with competing risks: consistency and rates of convergence of the MLE. Annals of Statistics 36, 1031-1063. (arXiv:math/0609020v2, published version)
  15. M.H. Maathuis and J.A. Wellner (2008). Inconsistency of the MLE for the joint distribution of interval censored survival times and continuous marks. Scandinavian Journal of Statistics 35, 83-103. (arXiv:math/0509084v2, published version)
  16. M.G. Hudgens, M.H. Maathuis and P.G. Gilbert (2007). Nonparametric estimation of the joint distribution of a survival time subject to interval censoring and a continuous mark. Biometrics 63, 372-380. (published version, supplementary information)
  17. M.H. Maathuis (2005). Reduction algorithm for the NPMLE for the distribution function of bivariate interval censored data. Journal of Computational and Graphical Statistics 14, 352-362. (arXiv:0906.3215v1, published version) (Winner of the 2004 student paper award of the ASA Statistical Computing and Graphics Sections)

Conference proceedings:

  1. M.H. Maathuis (2012). High-dimensional estimation of causal effects (extended abstract). Oberwolfach Report 14/2012, 22-24.
  2. D. Colombo, M.H. Maathuis, M. Kalisch and T.S. Richardson (2011). Learning high-dimensional DAGs with latent and selection variables (extended abstract). UAI 2011 (not-for-proceedings paper).

Theses:

  1. M.H. Maathuis (2006). Nonparametric estimation for current status data with competing risks. Ph.D. thesis, University of Washington.
  2. M.H. Maathuis (2003). Nonparametric maximum likelihood estimation for bivariate censored data. Master's thesis, Delft University of Technology, The Netherlands.

Other:

  1. Interview for "Memory Magazine, Tijdschrift voor ambitieuze studenten en starters", December 2008, p. 52-55 (in Dutch).
  2. Interview for ETH Globe, Juni 2008, p. 38-39 (ETH Globe is the quarterly magazine of ETH Zurich, in German).
  3. Interview for the publication Female professors at ETH Zurich (2007, in German).
  4. M.H. Maathuis (2005), "Schatten van de incubatietijd van AIDS", STAtOR, 6, nr 1-2, 35-39 (STAtOR is the magazine of the Dutch Society of Statistics and Operations Research (VVS), in Dutch).