• Meyer-Baese, A., Schmid, V.J.: Pattern Recognition and Signal Analysis in Medical Imaging. Academic Press (2014), 2nd edition. ISBN: 978-0-12-409545-8. [Link]

Peer-reviewed Journal Papers

  • Schmid, V.J., Cremer, M., Cremer, T.: Quantitative analyses of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. Methods (2017), in print, available online. [DOI]
  • Kühnl, A., Erk, A., Trenner, M., Salvermoser, M., Schmid, V., Eckstein, H.-H.: Incidence, treatment and mortality in patients with abdominal aortic aneurysms. Deutsches Aerzteblatt International (2017), 114 (22-23): 391-8. [DOI]
  • Schmidt, P., Mühlau, M., Schmid, V.: Fitting large-scale structured additive regression models using Krylov subspace methods. Computational Statistics & Data Analysis. 105 (2017) 59–75. [DOI]
  • Feilke, M., Bischl, B., Schmid, V.J., Gertheiss, J.: Boosting in Nonlinear Regression Models with an Application to DCE-MRI Data. Methods of Information in Medicine 55:1 (2016) 31–41. [DOI]
  • Popken J., Schmid V.J., Strauss, A., Guengoer, T., Wolf, E., Zakhartchenko, T.: Stage-dependent remodeling of the nuclear envelope and lamina during rabbit early embryonic development. Journal of Reproduction and Development 62:2 (2016) 127–135.  [DOI]
  • Popken J., Graf A., Krebs S., Blum H., Schmid V.J., Strauss, A., Guengoer, T., Zakhartchenko, T., Wolf, E., Cremer, T.: Remodeling of the Nuclear Envelope and Lamina during Bovine Preimplantation Development and Its Functional Implications. PLoS One 10:5 (2015) e0124619. [DOI]
  • Feilke, M., Schneider, K., Schmid, V.J.: Bayesian mixed-effect models for the analysis of a series of FRAP images. Statistical Applications in Genetics and Molecular Biology 14:1 (2015) 35–51. [DOI]
  • Popken, J., Brero, A., Koehler, D., Schmid, V.J., Strauss, A., Wuensch, A., Guengoer, T., Graf, A., Krebs, S., Blum, H., Zakhartchenko, V., Wolf, E., Cremer, T.: Reprogramming of fibroblast nuclei in cloned bovine embryos is paralleled by major structural remodeling with both striking similarities and differences to nuclear phenotypes of embryos fertilized in vitro. Nucleus 5:6 (2014) 555–589. [DOI]
  • Sommer, J., Schmid, V.J.: Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging. Journal of the Royal Statistical Society, Series C – Applied Statistics 63:5 (2014) 695–713. [DOI]
  • Guzman Castillo, M., Gillespie, D., Allen, K., Bandosz, P., Schmid, V., Capewell, S., O’Flaherty, M.: Future declines of Coronary Heart Disease mortality in England and Wales could counter the burden of population ageing. PLOS One (2014) 9(6): e99482. [DOI]
  • Smeets, D., Markaki, Y., Schmid, V.J., Kraus, F., Tattermusch, A., Cerase, A., Sterr, M., Fiedler, S., Demmerle, J., Popken, J., Leonhardt, H., Brockdorff, N., Cremer, T., Schermelleh, L., Cremer, M.: Three-dimensional super-resolution microscopy of the inactive X chromosome territory reveals a collapse of its active nuclear compartment harboring distinct Xist RNA foci. Epigenetics & Chromatin 7:8 (2014). [DOI]
  • Sommer, J., Gertheiss, J., Schmid, V.J.: Spatially regularized estimation for the analysis of DCE-MRI data. Statistics in Medicine 33:6 (2014) 1029-41. [DOI]
  • Jafari-Koshki, T., Schmid, V.J., Mahaki, B.: Trends of Cancer Incidence in Iran During 2004-2008: A Bayesian Space-Time Model. Asian Pacific Journal of Cancer Prevention 15:4 (2014) 1557-1561. [Link]
  • Schmidt, P., Schmid, V.J., Gaser, C., Buck, D., Bührlen, S., Föschler, A., Mühlau, M.: Fully Bayesian inference for structural MRI: application to segmentation and statistical analysis of T2-hypointensities. PLOS One 8:7 (2013) e68196. [DOI]
  • Lehermeier, C., Wimmer, V., Albrecht, T., Auinger, H.-J., Gianola, D., Schmid, V., Schön, C.-C.: Sensitivity to Prior Specification in Bayesian Genome-based Prediction Models. Statistical Applications in Genetics and Molecular Biology 12:3 (2013) 375-391. [DOI]
  • Schneider, K., Fuchs, C., Dobay, A., Rottach, A., Qin, W., Álvarez-Castro, J., Nalaskowski, M., Schmid, V., Leonhardt, H., Schermelleh, L.: Dissection of cell cycle dependent dynamics of Dnmt1 by FRAP and diffusion-coupled modeling. Nucleic Acids Research 41:9 (2013) 4860-4876. [DOI]
  • Norousi, R., Wickles, S., Leidig, C., Tresch, A., Schmid, V.J., Beckmann, R., Becker, T.: Automatic post-picking using MAPPOS improves particle image detection from Cryo- EM micrographs. Journal of Structural Biology 182:2 (2013) 59-66. [DOI]
  • Markaki, Y., Smeets, D., Fiedler, S., Schmid, V.J., Schermelleh, L., Cremer, T., Cremer, M.: The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture. BioEssays 34:5 (2012) 412-426. [DOI]
  • Copley, S.J., Giannarou, S., Schmid, V.J., Hansell, D.M., Wells, A.U., Yang, G.-Z.: Effect of Ageing on Lung Microstructure in vivo: Assessment with Densitometric and Textural Analysis of High resolution CT Data. Journal of Thoracic Imaging 27:6 (2012) 366-371. [DOI]
  • Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler A., Berthele, A. Hoshi, M., Ilg, R., Schmid, V.J., Zimmer, C., Hemmer, B., Mühlau, M.: An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage 59:4 (2012) 3774-3783. [DOI]
  • Mahaki, B., Mehrabi, Y., Kavousi, A., Akbari, M.E., Waldhoer, T., Schmid, V.J., Yaseri, M.: Multivariate Disease Mapping of Seven Prevalent Cancers in Iran using a Shared Component Model. Asian Pacific Journal of Cancer Prevention 12:9 (2011), 2353-2358 [Link]
  • Seiler, D.M., Rouquette, J., Schmid, V.J., Strickfaden, H., Ottmann, C., Drexler, G.A, Mazurek, B., Greubel, C., Hable, V., Dollinger, G., Cremer, T., Friedl, A.A.: Double-strand break-induced transcriptional silencing is associated with loss of tri-methylation at H3K4. Chromosome Research 19:7 (2011) 883-899. [DOI]
  • Whitcher, B., Schmid, V.J.: Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R. Journal of Statistical Software 44:5 (2011). [Link]
  • Whitcher, B., Schmid, V.J., Thornton, A.: Working with the DICOM and NIfTI Data Standards in R. Journal of Statistical Software 44:6 (2011). [Link]
  • Schmid, V.J.: Voxel based adaptive spatio-temporal modelling of perfusion cardiovascular MRI. IEEE Transactions on Medical Imaging 30:7 (2011) 1305-1313.[DOI]
  • Staubach, C., Hoffmann, L., Schmid, V.J., Ziller, M., Tackmann, K., Conraths, F.J.: Bayesian space–time analysis of Echinococcus multilocularis-infections in foxes. Veterinary Parasitology 179:1-3 (2011) 77-83. [DOI]
  • K. Tabelow, J.D. Clayden, P. Lafaye de Micheaux, J. Polzehl, V.J. Schmid, B. Whitcher: Image Analysis and Statistical Inference in Neuroimaging with R. NeuroImaging 55:4 (2011) 1686-1693. [DOI]
  • Whitcher, B., Schmid, V.J., Collins, D.J., Orton, M.R., Koh, D.-M., Diaz de Corcuera, I., Parera, M., del Campo, J.M., deSouza, N.M., Leach, M., Harrington, K., El-Hariry, I.A.: A Bayesian Hierarchical Model for Dynamic Contrast-Enhanced MRI: A Phase II Study in Advanced Squamous Cell Carcinoma of the Head and Neck. Magnetic Resonance Materials in Physics, Biology and Medicine 24:2 (2011) 85-96. [DOI]
  • Schmid, V.J., Whitcher, B., Yang, G.Z.: Quantitative analysis of Dynamic contrast-enhanced MR images based on Bayesian P-Splines. IEEE Transactions on Medical Imaging 28 (2009) 789-798 [DOI]
  • Schmid, V.J., Whitcher, B., Padhani, A.R., Taylor, N.J., Yang, G.Z.: A Bayesian Hierarchical Model for the Analysis of a Longitudinal Dynamic Contrast-Enhanced MRI Cancer Study. Magnetic Resonance in Medicine 61 (2009) 163-174 [DOI]
  • Schmid, V.J., Held, L.: BAMP – Bayesian age-period-cohort modeling and prediction. Journal of Statistical Software 21 (2007) [Link]
  • Schmid, V.J., Whitcher, B., Padhani, A.R., Taylor, N.J., Yang, G.Z.: Bayesian methods for pharmacokinetic models in dynamic contrastenhanced magnetic resonance imaging. IEEE Transactions on Medical Imaging 25 (2006) 1627-1636 [DOI]
  • Held, L., Hofmann, M., Höhle, M., Schmid, V.: A two component model for counts of infectious diseases. Biostatistics 7 (2006) 422–437 [DOI]
  • Lopez, A., Shibuya, K., Rao, C., Mathers, C., Hansell, A., Held, L., Schmid, V., Buist, S.: Chronic obstructive pulmonary disease: current burden and future projections. European Respiratory Journal 27 (2006) 397–412 [DOI]
  • Schmid, V., Held, L.: Bayesian extrapolation of space-time trends for cancer registry data. Biometrics 60 (2004) 1034–1042 [DOI]
  • Staubach, C., Schmid, V., Knorr-Held, L., Ziller, M.: A Bayesian model for spatial wildlife disease prevalence data. Preventive Veterinary Medicine 56 (2002) 75–87 [DOI]

Book chapters

  • Schmid, V.J.: Kinetic Models for Cancer Imaging. In: Hamid R. Arabnia: Advances in Computational Biology. Heidelberg: Springer (2010). ISBN 978-1-4419-5912-6 [DOI] – Erratum [DOI]

Peer-reviewed Proceedings

  • Mohajer, M., Schmid, V.J., Engels, N.A., Noel, P.B., Rummeney, E., Englmeier, K.H.: Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets. In: SPIE Medical Imaging (2012) 8317 [DOI]
  • Mohajer, M., Schmid, V.J., Braren, R., Noel, P.B., Englmeier, K.H.: How Heterogeneous is the Liver? A Cluster Analyse of DCE-MRI Time Series. NSS-MIC 2011,p. 2483-2487 [DOI]
  • Kärcher, J., Schmid, V.J.: Two tissue compartment model in DCE-MRI: A Bayesian Approach. In: IEEE International Symposium on Biomedical Imaging. From Nano to Macro (2010) 724-727 [DOI]
  • Schmid, V.J., Gatehouse, P.D. Yang, G.Z.: Attenuation resilient AIF estimation based on hierarchical Bayesian modelling for first pass myocardial perfusion MRI. In: N.Ayache, S.Ourselin, A.Maeder (Eds.).: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007, Part I, LNCS 4791, Berlin: Springer (2007) 393-400 [DOI]
  • Schmid, V.J., Whitcher, B., Yang, G.Z.: Semi-parametric analysis of dynamic contrast-enhanced MRI using Bayesian P-splines. In Larsen, R., Nielsen, M., Sporring, J., eds.: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. Number 4190 in Lecture Notes in Computer Science, Berlin: Springer (2006) 679–686 [DOI]
  • Schmid, V.J., Whitcher, B., Yang, G.Z., Taylor, N.J., Padhani, A.R.: Statistical analysis of pharmacokinetical models in dynamic contrast-enhanced magnetic resonance imaging. In Duncan, J., Gerig, G., eds.: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. Number 3750 in Lecture Notes in Computer Science, Berlin: Springer (2005) 886–893 [DOI]


  • Schmid, V.: Bayesianische Raum-Zeit-Modellierung in der Epidemiologie (Bayesian spatio-temporal modeling in epidemiology). München: Dr.Hut Verlag (2004). PhD thesis, Fakultät für Mathematik, Informatik und Statistik der Ludwig-Maximilians-Universität München. ISBN 3-89963-113-7 [Buy] [Online]
  • Schmid, V.: Räumliche Erweiterungen von Bayesianischen Alters-Perioden-Kohorten-Modellen (Spatial extensions of Bayesian age-period-cohort models). Diplomarbeit (Master thesis), Institut für Statistik der Ludwig-Maximilians-Universität München (1999) [PDF]

Selected Technical Reports and Other Publications

  • Mojgan, M., Englmeier, K.H., Schmid, V.J.: A comparison of Gap statistic definitions with and without logarithm function. Department of Statistics: Technical Reports, Nr. 96 (2010) [Link]. arXiv:1103.4767
  • Fahrmeir, L., Belitz, C., Brezger, A., Hennerfeind, A., Jerak, A., Schmid, V.: Gutachten zur Erstellung des Mietspiegels für München 2005, Teil 2: Statistische Analyse der Nettomieten. Landeshauptstadt München, Sozialreferat – Amt für Wohnungswesen (2005) [Link]
  • Schmid, V., Hansel, A., Held, L.: Some issues in Bayesian age period cohort models. Technical Report 404, SFB 386, University Munich (2004)
  • Fahrmeir, L., Biller, C., Brezger, A., Gieger, C., Hennerfeind, A., Jerak, A., Schmid, V.: Gutachten zur Erstellung des Mietspiegels für München 2003, Teil 2: Statistische Analyse der Nettomieten. Landeshauptstadt München, Sozialreferat – Amt für Wohnungswesen (2003) [Link]



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