Publications

[1]

S. Bark, W. A. Malik, M. Prus, H.-P. Piepho, and V. J. Schmid, “A bayesian updating framework for long-term multi-environment trial data in plant breeding,” arXiv preprint arXiv:2604.16203, Apr. 2026, doi: 10.48550/arXiv.2604.16203.

[2]

A. Kuehnl, V. J. Schmid, M. Olm, and W. Weber, “Baby boomers in germany: A secondary data analysis of demographics, regional disparities, healthcare utilization, and mortality,” in BMC public health 26, 1228 (2026), Apr. 2026. doi: 10.1186/s12889-026-27245-z.

Related project: Baby Boomers in Germany

[3]

R. F. Hassankiadeh, A. Dobson, S. Rahimi, A. Jalilian, V. J. Schmid, and B. Mahaki, “Spatial distribution and birth prevalence of congenital heart disease in iran: A systematic review and hierarchical bayesian meta-analysis,” International Journal of Health Policy and Management, 13 (1), pp. 1–11, Jan. 2024, doi: 10.34172/ijhpm.2024.7931.

Related project: Disease Mapping

[4]

M. A. Abdulrahman, M. Bhattacharjee, and V. J. Schmid, “Bayesian spatial analysis for breast and prostate cancer incidence in sudan based on 2009 - 2013 national registry data,” Napata Scientific Journal (2) 2 , pp. 135–-151, Dec. 2023, doi: 10.53796/nsj22/2.

Related project: Disease Mapping

[5]

Z. A. Farsani and V. J. Schmid, “Maximum entropy technique and regularization functional for determining the pharmacokinetic parameters in DCE-MRI,” Journal of Digital Imaging, 35, pp. 1176–1188, May 2022, doi: 10.1007/s10278-022-00646-3.

[6]

Z. A. Farsani and V. J. Schmid, “Modified maximum entropy method and estimating the AIF via DCE-MRI data analysis,” Entropy (2022), 24:2, 155, Jan. 2022, doi: 10.3390/e24020155.

[7]

Y. Horikoshi, H. Shima, J. Sun, W. Kobayashi, V. J. Schmid, H. Ochiai, L. Shi, A. Fukuto, Y. Kinugasa, H. Kurumizaka, T. Ikura, Y. Markaki, S. Tate, K. Igarashi, T. Cremer, and S. Tashiro, “Distinctive nuclear zone for RAD51-mediated homologous recombinational DNA repair.” bioRxiv (2021), Nov. 2021, doi: 10.1101/2021.11.29.470307.

[8]

A. Erk, M. Trenner, M. Salvermoser, B. Reutersberg, V. J. Schmid, H.-H. Eckstein, and A. Kuehnl, “Zusammenhang zwischen der regionalen siedlungsstruktur und der krankenhausinzidenz, therapieform und mortalität von nicht-rupturierten abdominalen aortenaneurysmen. Sekundärdatenanalyse der deutschen DRG-statistik von 2005–2014,” Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, vol. 163, pp. 38–46, Jun. 2021, doi: 10.1016/j.zefq.2021.02.011.

Related project: Abdominal Aortic Aneurysm (AAA)

[9]

Z. A. Farsani and V. J. Schmid, “Co-localization analysis in fluorescence microscopy via maximum entropy copula,” International Journal of Biostatistics, (17) 1, pp. 165–175, May 2021, doi: 10.1515/ijb-2019-0019.

[10]

A. Erk, M. Trenner, M. Salvermoser, B. Reutersberg, V. J. Schmid, H.-H. Eckstein, and A. Kuehnl, “Relationship between regional settlement structure and hospital incidence, type of therapy and mortality of non-ruptured abdominal aortic aneurysms,” Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, 163, pp. 38–46, Jan. 2021, doi: 10.1016/j.zefq.2021.02.011.

Related project: Abdominal Aortic Aneurysm (AAA)

[11]

M. Yazdi, R. Kelishadi, V. J. Schmid, M.-E. Motlagh, R. Heshmat, and M. Mansourian, “Geographic risk of general and abdominal obesity and related determinants in iranian children and adolescents: CASPIAN-IV study,” Eastern Mediterranean Health Journal, 26 (12), pp. 1532–1538, Dec. 2020, doi: 10.26719/emhj.20.054.

Related project: Disease Mapping

[12]

M. Trenner, M. Salvermoser, A. Busch, V. J. Schmid, H.-H. Eckstein, and A. Kuehnl, “The effects of minimum caseload requirements on management and outcome in abdominal aortic aneurysm repair,” Dtsch Arztebl International (2020), pp. 820–-827, Nov. 2020, doi: 10.3238/arztebl.2020.0820.

Related project: Abdominal Aortic Aneurysm (AAA)

[13]

M. Cremer, K. Brandstetter, A. Maiser, and Suhas S P Rao, V. J. Schmid, N. Mitra, S. Mamberti, K.-N. Klein, D. M. Gilbert, H. Leonhardt, M. C. Cardoso, E. L. Aiden, H. Harz, and T. Cremer, “Cohesin depleted cells pass through mitosis and reconstitute a functional nuclear architecture.” Nature Communications 11, 6146 (2020), Nov. 2020, doi: 10.1038/s41467-020-19876-6.

[14]

M. Trenner, M. Salvermoser, B. Reutersberg, A. Busch, V. J. Schmid, H.-H. Eckstein, and A. Kuehnl, “Regional variation in endovascular treatment rate and in-hospital mortality of abdominal aortic aneurysms in germany. Secondary data analysis of nationwide hospital DRG data from 2012 to 2014,” Vasa (2019), pp. 1–-8, Nov. 2019, doi: 10.1024/0301-1526/a000830.

Related project: Abdominal Aortic Aneurysm (AAA)

[15]

M. Raei, V. J. Schmid, M. Moayyed, and B. Mahaki, “Spatio-temporal pattern of two common cancers among iranian woman: An adaptive smoothing model,” J BUON, vol. 24, no. 3, pp. 1268–1275, Jan. 2019, Available: https://jbuon.com/archive/24-3-1268.pdf

Related project: Disease Mapping

[16]

C. Happ, S. Greven, and V. J. Schmid, “The impact of model assumptions in scalar-on-image regression,” Statistics in Medicine, vol. 19, pp. 4298–4317, Aug. 2018, doi: 10.1002/sim.7915.

[17]

A. Kuehnl, M. Salvermoser, E. Knipfer, A. Zimmermann, V. J. Schmid, and H.-H. Eckstein, “Regionale haeufigkeit von revaskularisierenden prozeduren bei karotisstenose in deutschland,” Gefaesschirurgie 23(27–28), 2018., Jul. 2018, doi: 10.1007/s00772-018-0385-9.

Related project: Abdominal Aortic Aneurysm (AAA)

[18]

M. Raei, V. J. Schmid, and B. Mahaki, “Bivariate spatiotemporal disease mapping of cancer of the breast and cervix uteri among iranian women,” Geospat Health, vol. 13, no. 1, pp. 164–171, Jan. 2018, doi: 10.4081/gh.2018.645.

Related project: Disease Mapping

[19]

M. Trenner, A. Kuehnl, M. Salvermoser, B. Reutersberg, S. Geisbuesch, V. J. Schmid, and H.-H. Eckstein, “Editors choice – high annual hospital volume is associated with decreased in hospital mortality and complication rates following treatment of abdominal aortic aneurysms: Secondary data analysis of the nationwide german DRG statistics from 2005 to 2013,” European Journal of Vascular and Endovascular Surgery, vol. 55, no. 2, pp. 185–194, Jan. 2018, doi: 10.1016/j.ejvs.2017.11.016.

Related project: Abdominal Aortic Aneurysm (AAA)

[20]

B. Mahaki, Y. Mehrabi, A. Kavousi, and V. J. Schmid, “Joint spatio-temporal shared component model with an application in iran cancer data,” Asian Pacific Journal of Cancer Prevention, vol. 19, pp. 1553–1560, Jan. 2018, doi: 10.22034/APJCP.2018.19.6.1553.

Related project: Disease Mapping

[21]

A. Kuehnl, M. Salvermoser, A. Erk, M. Trenner, V. J. Schmid, and H.-H. Eckstein, “Spatial analysis of hospital incidence and in hospital mortality of abdominal aortic aneurysms in germany: Secondary data analysis of nationwide hospital episode (DRG) data,” European Journal of Vascular and Endovascular Surgery, vol. 55, no. 6, pp. 852–859, Jan. 2018, doi: 10.1016/j.ejvs.2018.02.024.

Related projects: Abdominal Aortic Aneurysm (AAA), Disease Mapping

[22]

A. Kuehnl, A. Erk, M. Trenner, M. Salvermoser, V. J. Schmid, and H.-H. Eckstein, “Inzidenz, therapie und letalitat abdominaler aortenaneurysmen,” Deutsches Aerzteblatt International, vol. 114, no. 22–23, pp. 391–398, Jan. 2017, doi: 10.3238/arztebl.2017.0391.

Related project: Abdominal Aortic Aneurysm (AAA)

[23]

M. Cremer, V. J. Schmid, F. Kraus, Y. Markaki, I. Hellmann, A. Maiser, H. Leonhardt, S. John, J. Stamatoyannopoulos, and T. Cremer, “Initial high-resolution microscopic mapping of active and inactive regulatory sequences proves non-random 3D arrangements in chromatin domain clusters,” Epigenetics and Chromatin, vol. 10, no. 1, Jan. 2017, doi: 10.1186/s13072-017-0146-0.

[24]

V. J. Schmid, M. Cremer, and T. Cremer, “Quantitative analyses of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy,” Methods, vol. 123, pp. 33–46, Jan. 2017, doi: 10.1016/j.ymeth.2017.03.013.

[25]

P. Schmidt, M. Mühlau, and V. J. Schmid, “Fitting large-scale structured additive regression models using krylov subspace methods,” Computational Statistics and Data Analysis, vol. 105, pp. 59–75, Jan. 2017, doi: 10.1016/j.csda.2016.07.006.

[26]

Z. A. Farsani and V. J. Schmid, “Maximum entropy approach in dynamic contrast-enhanced magnetic resonance imaging,” Methods of Information in Medicine, vol. 56, no. 6, pp. 461–468, Jan. 2017, doi: 10.3414/ME17-01-0027.

[27]

R. Norousi and V. J. Schmid, “Automatic 3D object detection of proteins in fluorescent labeled microscope images with spatial statistical analysis,” arXiv preprint arXiv:1601.01216, Jan. 2016, doi: 10.48550/arXiv.1601.01216.

[28]

J. Popken, V. J. Schmid, A. Strauss, T. Guengoer, E. Wolf, and V. Zakhartchenko, “Stage-dependent remodeling of the nuclear envelope and lamina during rabbit early embryonic development,” Journal of Reproduction and Development, vol. 62, no. 2, pp. 127–135, Jan. 2016, doi: 10.1262/jrd.2015-100.

[29]

M. Feilke, B. Bischl, V. J. Schmid, and J. Gertheiss, “Boosting in nonlinear regression models with an application to DCE-MRI data,” Methods of Information in Medicine 2016; 55(01): 31-41, Jan. 2016, doi: 10.3414/ME14-01-0131.

Related project: Software for Magnetic Resonance Imaging

[30]

M. Feilke, K. Schneider, and V. J. Schmid, “Bayesian mixed-effects model for the analysis of a series of FRAP images,” Statistical Applications in Genetics and Molecular Biology, vol. 14, no. 1, pp. 35–41, Jan. 2015, doi: 10.1515/sagmb-2014-0013.

[31]

J. Popken, A. Graf, S. Krebs, H. Blum, V. J. Schmid, A. Strauss, T. Guengoer, V. Zakhartchenko, E. Wolf, and T. Cremer, “Remodeling of the nuclear envelope and lamina during bovine preimplantation development and its functional implications,” PLOS ONE, vol. 10, no. 5, p. e0124619, Jan. 2015, doi: 10.1371/journal.pone.0124619.

[32]

J. C. Sommer, J. Gertheiss, and V. J. Schmid, “Spatially regularized estimation for the analysis of dynamic contrast-enhanced magnetic resonance imaging data,” Statistics in Medicine, vol. 33, no. September 2012, pp. 1029–1041, 2014, doi: 10.1002/sim.5997.

[33]

A. Meyer-Baese and V. J. Schmid, Pattern recognition and signal analysis in medical imaging. Academic Press 2014, 2014. doi: 10.1016/b978-0-12-409545-8.00014-5.

[34]

M. G. Castillo, D. O. S. Gillespie, K. Allen, P. Bandosz, V. J. Schmid, S. Capewell, and M. O’Flaherty, “Future declines of coronary heart disease mortality in england and wales could counter the burden of population ageing,” PLoS ONE, vol. 9, no. 6, p. e99482, Jan. 2014, doi: 10.1371/journal.pone.0099482.

[35]

T. Jafari-Koshki, V. J. Schmid, and B. Mahaki, “Trends of breast cancer incidence in iran during 2004-2008: A bayesian space-time model,” Asian Pacific Journal of Cancer Prevention, vol. 15, no. 4, pp. 1557–1561, Jan. 2014, doi: 10.7314/APJCP.2014.15.4.1557.

Related project: Disease Mapping

[36]

J. Popken, A. Brero, D. Koehler, V. J. Schmid, A. Strauss, A. Wuensch, T. Guengoer, A. Graf, S. Krebs, H. Blum, V. Zakhartchenko, E. Wolf, and T. Cremer, “Reprogramming of fibroblast nuclei in cloned bovine embryos involves major structural remodeling with both striking similarities and differences to nuclear phenotypes of in vitro fertilized embryos,” Nucleus, vol. 5, no. 6, pp. 555–589, Jan. 2014, doi: 10.4161/19491034.2014.979712.

[37]

D. Smeets, Y. Markaki, V. J. Schmid, F. Kraus, A. Tattermusch, A. Cerase, M. Sterr, S. Fiedler, J. Demmerle, J. Popken, H. Leonhardt, N. Brockdorff, T. Cremer, L. Schermelleh, and M. Cremer, “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, vol. 7, no. 1, p. 8, Jan. 2014, doi: 10.1186/1756-8935-7-8.

[38]

J. C. Sommer and V. J. Schmid, “Spatial two-tissue compartment model for dynamic contrast-enhanced magnetic resonance imaging,” Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 63, no. 5, pp. 695–713, Jan. 2014, doi: 10.1111/rssc.12057.

Related project: Software for Magnetic Resonance Imaging

[39]

P. Schmidt, V. J. Schmid, C. Gaser, D. Buck, S. Buhrlen, A. Forschler, and M. Mühlau, “Fully bayesian inference for structural MRI: Application to segmentation and statistical analysis of T2-hypointensities,” PLoS ONE, vol. 8, no. 7: e68196, Jan. 2013, doi: 10.1371/journal.pone.0068196.

Related project: Software for Magnetic Resonance Imaging

[40]

R. Norousi, S. Wickles, C. Leidig, T. Becker, V. J. Schmid, R. Beckmann, and A. Tresch, “Automatic post-picking using MAPPOS improves particle image detection from cryo-EM micrographs,” Journal of Structural Biology, vol. 182, no. 2, pp. 59–66, Jan. 2013, doi: 10.1016/j.jsb.2013.02.008.

[41]

K. Schneider, C. Fuchs, A. Dobay, A. Rottach, W. Qin, P. Wolf, J. M. Alvarez-Castro, M. M. Nalaskowski, E. Kremmer, V. J. Schmid, H. Leonhardt, L. Schermelleh, and C. Dargatz, “Dissection of cell cycle dependent dynamics of Dnmt1 by FRAP and diffusion-coupled modeling,” Nucleic Acids Research, vol. 41, no. 9, pp. 4860–4876, Jan. 2013, doi: 10.1093/nar/gkt191.

[42]

C. Lehermeier, V. Wimmer, T. Albrecht, H.-J. Auinger, D. Gianola, V. J. Schmid, and C.-C. Schon, “Sensitivity to prior specification in bayesian genome-based prediction models,” Statistical Applications in Genetics and Molecular Biology, vol. 12, no. 3, pp. 1–32, Jan. 2013, doi: 10.1515/sagmb-2012-0042.

[43]

P. Schmidt, C. Gaser, M. Arsic, D. Buck, A. Forschler, A. Berthele, M. Hoshi, R. Ilg, V. J. Schmid, C. Zimmer, B. Hemmer, and M. Muhlau, “An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis,” NeuroImage, vol. 59, no. 4, pp. 3774–3783, Jan. 2012, doi: 10.1016/j.neuroimage.2011.11.032.

Related project: Software for Magnetic Resonance Imaging

[44]

S. J. S. Copley, S. Giannarou, V. J. Schmid, D. M. Hansell, A. U. Wells, and G.-Z. Yang, “Effect of aging on lung structure in vivo: Assessment with densitometric and fractal analysis of high-resolution computed tomography data.” Journal of thoracic imaging, Jan. 2012, doi: 10.1097/RTI.0b013e31825148c9.

Related project: Software for Magnetic Resonance Imaging

[45]

M. Mohajer, V. J. Schmid, N. A. Engels, P. B. Noel, E. Rummeny, K.-H. Englmeier, N. A. Engels, P. B. Noel, V. J. Schmid, and K.-H. Englmeier, “Stepwise heterogeneity analysis of breast tumors in perfusion DCE-MRI datasets,” Jan. 2012. doi: 10.1117/12.910499.

[46]

Y. Markaki, D. Smeets, S. Fiedler, V. J. Schmid, L. Schermelleh, T. Cremer, and M. Cremer, “The potential of 3D-FISH and super-resolution structured illumination microscopy for studies of 3D nuclear architecture: 3D structured illumination microscopy of defined chromosomal structures visualized by 3D (immuno)-FISH opens new perspectives for studies of nuclear architecture,” BioEssays : news and reviews in molecular, cellular and developmental biology, vol. 34, no. 5, pp. 412–26, Jan. 2012, doi: 10.1002/bies.201100176.

[47]

M. Mohajer, K. Englmeier, and V. J. Schmid, “A comparison of gap statistic definitions with and without logarithm function,” arXiv preprint arXiv:1103.4767, pp. 1–11, Jan. 2011, doi: 10.48550/arXiv.1103.4767.

[48]

B. Mahaki, Y. Mehrabi, A. Kavousi, M. E. Akbari, T. Waldhoer, V. J. Schmid, and M. Yaseri, “Multivariate disease mapping of seven prevalent cancers in iran using a shared component model.” Asian Pacific journal of cancer prevention : APJCP, vol. 12, no. 9, pp. 2353–8, Jan. 2011, Available: http://journal.waocp.org/article_25886_ea6373925ce69ed33c65dfee7f2b0e15.pdf

Related project: Disease Mapping

[49]

K. Tabelow, J. D. Clayden, P. L. de Micheaux, J. Polzehl, V. J. Schmid, and B. J. Whitcher, “Image analysis and statistical inference in neuroimaging with r,” NeuroImage, vol. 55, no. 4, pp. 1686–1693, Jan. 2011, doi: 10.1016/j.neuroimage.2011.01.013.

Related project: Software for Magnetic Resonance Imaging

[50]

B. J. Whitcher and V. J. Schmid, “Quantitative analysis of dynamic contrast-enhanced and diffusion-weighted magnetic resonance imaging for oncology in r,” Journal of Statistical Software, vol. 44, no. 5, pp. 1–29, Jan. 2011, doi: http://dx.doi.org/10.18637/jss.v044.i05.

Related project: Software for Magnetic Resonance Imaging

[51]

V. J. Schmid, “Voxel-based adaptive spatio-temporal modelling of perfusion cardiovascular MRI.” IEEE transactions on medical imaging, vol. 30, no. 7, pp. 1305–13, Jan. 2011, doi: 10.1109/TMI.2011.2109733.

Related project: Software for Magnetic Resonance Imaging

[52]

B. J. Whitcher, V. J. Schmid, and A. Thornton, “Working with the DICOM and NIfTI data standards in r,” Journal of Statistical Software, vol. 44, no. 6, pp. 1–28, Jan. 2011, doi: 10.18637/jss.v044.i06.

Related project: Software for Magnetic Resonance Imaging

[53]

D. M. Seiler, J. Rouquette, V. J. Schmid, H. Strickfaden, C. Ottmann, G. a Drexler, B. Mazurek, C. Greubel, V. Hable, G. Dollinger, T. Cremer, and A. a Friedl, “Double-strand break-induced transcriptional silencing is associated with loss of tri-methylation at H3K4.” Chromosome research: an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology, pp. 883–899, Jan. 2011, doi: 10.1007/s10577-011-9244-1.

[54]

C. Staubach, L. Hoffmann, V. J. Schmid, M. Ziller, K. Tackmann, and F. J. Conraths, “Bayesian space-time analysis of echinococcus multilocularis-infections in foxes.” Veterinary parasitology, vol. 179, no. 1–3, pp. 77–83, Jan. 2011, doi: 10.1016/j.vetpar.2011.01.065.

Related project: Disease Mapping

[55]

B. J. Whitcher, V. J. Schmid, D. J. Collins, M. R. Orton, D. M. Koh, I. D. D. Corcuera, M. Parera, J. M. D. Campo, N. M. Desouza, M. O. Leach, K. Harrington, and I. A. El-Hariry, “A bayesian hierarchical model for DCE-MRI to evaluate treatment response in a phase II study in advanced squamous cell carcinoma of the head and neck,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 24, no. 2, pp. 85–96, Jan. 2011, doi: 10.1007/s10334-010-0238-3.

Related project: Software for Magnetic Resonance Imaging

[56]

M. Mohajer and V. J. Schmid, “How heterogeneous is the liver? A cluster analysis of DCE-MRI time series,” Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2011 IEEE, pp. 2483–2487, Jan. 2011, doi: 10.1109/nssmic.2011.6152673.

Related project: Software for Magnetic Resonance Imaging

[57]

J. C. Karcher and V. J. Schmid, “Two tissue compartment model in DCE-MRI: A bayesian approach,” in 2010 IEEE international symposium on biomedical imaging: From nano to macro, 3, pp. 724–727, Jan. 2010. doi: 10.1109/ISBI.2010.5490074.

Related project: Software for Magnetic Resonance Imaging

[58]

J. Gertheiss, J. C. Kärcher, and V. J. Schmid, “Analysis of DCE-MRI data using a nonnegative elastic net,” Department of Statistics, Ludwig-Maximilians-Universität München, 90, 2010. doi: 10.5282/ubm/epub.11809.

Related project: Software for Magnetic Resonance Imaging

[59]

V. J. Schmid, B. Whitcher, A. R. Padhani, N. J. Taylor, and G.-Z. Yang, “A bayesian hierarchical model for the analysis of a longitudinal dynamic contrast-enhanced MRI oncology study,” Magnetic Resonance in Medicine, vol. 61, no. 1, pp. 163–174, 2009, doi: 10.1002/mrm.21807.

Related project: Software for Magnetic Resonance Imaging

[60]

V. J. Schmid and G.-Z. Yang, “Spatio-temporal modelling of first-pass perfusion cardiovascular MRI,” World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany, vol. 25, no. 4, pp. 45–48, Jan. 2008, doi: 10.1007/978-3-642-03882-2_12.

Related project: Software for Magnetic Resonance Imaging

[61]

V. J. Schmid, P. D. Gatehouse, and G.-Z. Yang, “Attenuation resilient AIF estimation based on hierarchical bayesian modelling for first pass myocardial perfusion MRI,” in In: Ayache n., ourselin s., maeder a. (Eds) medical image computing and computer-assisted intervention – MICCAI 2007. MICCAI 2007. Lecture notes in computer science, vol 4791. Springer, berlin, heidelberg, pp. 393–400, Jan. 2007. doi: 10.1007/978-3-540-75757-3_48.

Related project: Software for Magnetic Resonance Imaging

[62]

V. J. Schmid and L. Held, “Bayesian age-period-cohort modeling and prediction - BAMP,” Journal of Statistical Software, vol. 21, no. 8, pp. 1–15, 2007, doi: 10.18637/jss.v021.i08.

[63]

L. Held, M. Hofmann, M. Hohle, and V. J. Schmid, “A two-component model for counts of infectious diseases.” Biostatistics, vol. 7, no. 3, pp. 422–37, Jan. 2006, doi: 10.1093/biostatistics/kxj016.

[64]

A. Lopez, K. Shibuya, C. Rao, C. Mathers, A. L. Hansell, L. Held, V. J. Schmid, and S. Buist, “Chronic obstructive pulmonary disease: Current burden and future projections,” European Respiratory Journal, vol. 27, no. 2, p. 397, Jan. 2006, doi: 10.1183/09031936.06.00025805.

[65]

V. J. Schmid, B. J. Whitcher, and G.-Z. Yang, “Semi-parametric analysis of dynamic contrast-enhanced MRI using bayesian p-splines,” in N 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, pp. 679–686, Jan. 2006. doi: 10.1007/11866565_83.

Related project: Software for Magnetic Resonance Imaging

[66]

V. J. Schmid, B. J. Whitcher, G.-Z. Yang, N. J. Taylor, and A. R. Padhani, “Statistical analysis of pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging.” Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 8, no. Pt 2, pp. 886–893, Jan. 2005, doi: 10.1007/11566489_109.

Related project: Software for Magnetic Resonance Imaging

[67]

V. J. Schmid, Bayesianische raum-zeit-modellierung in der epidemiologie. Dr. Hut Verlag 2004, 2004. Available: https://edoc.ub.uni-muenchen.de/3000/1/Schmid_Volker.pdf

[68]

V. J. Schmid and L. Held, “Bayesian extrapolation of space–time trends in cancer registry data,” Biometrics, vol. 60, no. December, pp. 1034–1042, Jan. 2004, doi: 10.1111/j.0006-341X.2004.00259.x.

[69]

C. Staubach, V. J. Schmid, L. Knorr-Held, and M. Ziller, “A bayesian model for spatial wildlife disease prevalence data,” Preventive Veterinary Medicine, vol. 56, no. 1, pp. 75–87, Jan. 2002, doi: 10.1016/S0167-5877(02)00125-3.

Related project: Disease Mapping

[70]

A. L. Hansell, L. Knorr-Held, N. G. Best, V. J. Schmid, and P. Aylin, “COPD mortality trends 1950-1999 in england & wales - did the 1956 clean air act make a detectable difference?” Jan. 2002, p. S55. doi: 10.1097/00001648-200309001-00118.