Prof. Dr. Timo Schmid

Inhaber des Lehrstuhls für Statistik und Ökonometrie

Otto-Friedrich-Universität Bamberg
Fakultät für Sozial- und Wirtschaftswissenschaften
Raum F21/00.76b
Feldkirchenstraße 21
96045 Bamberg

Tel.: 0951-863-2530

E-Mail

Sprechstunde: nach Vereinbarung

Curriculum Vitae(155.5 KB)
 

Forschungsgebiete

• Survey‐Statistik, insbesondere statistische Modellierung
• Indizes und Sozialindikatoren, insbesondere Armutsmessung
• Nutzung großer Datenmengen/ Big Data (etwa Mobilfunkdaten) in der Statistik
• Small Area Estimation
• Simulationstechniken und Monte‐Carlo‐Methoden
• Räumliche Analyseverfahren
• Estimation of Wealth Using HFCS Data

Kurzbiographie

Timo Schmid leitet seit 2021 den Lehrstuhl für Statistik und Ökonometrie an der Otto-Friedrich-Universität Bamberg sowie das Bamberger Centrum für Empirische Studien (BACES). Vor seinem Ruf an die Universität Bamberg war Timo Schmid verantwortlich für den Lehrstuhl für Angewandte Statistik an der Freien Universität Berlin und leitete die statistische Beratung der Freien Universität Berlin. Timo Schmid studierte Mathematik an der Universität Tübingen und war im Anschluss als Berater bei der Unternehmensberatung A.T. Kearney tätig. Von 2010 bis 2012 promovierte Timo Schmid am Lehrstuhl für Wirtschafts- und Sozialstatistik an der Universität Trier und wurde durch ein Promotionsstipendium der Stiftung der Deutschen Wirtschaft (sdw) gefördert.

Timo Schmid ist Mitglied des Vorstandes der Deutschen Statistischen Gesellschaft (DStatG) und Herausgeber der Zeitschrift Wirtschafts- und Sozialstatistisches Archiv der DStatG.

Ausgewählte Veröffentlichungen

• Small Area with Multiply Imputed Survey Data, with Runge, M., Journal of Official Statistics, 2023, 39, pp. 507-533.

• A Framework for Producing Small Area Estimates Based on Area-Level Models in R, with Harmening, S., Kreutzmann, A.-K., Salvati, N., Schmidt, S., The R Journal, 2023, 15, pp. 316-341.

• Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany, with Hadam, S., Würz, N. and Kreutzmann, A., Statistical Methods & Applications, 2023.
• Variable selection using conditional AIC for linear mixed models with data-driven transformations, with Lee, Y., Rojas-Perilla, N., Runge, M., Statistics and Computing, 2023, forthcoming.
• Flexible domain prediction using mixed effects random forests, with Krennmair, P., Journal of the Royal Statistical Society Series C, 2023, forthcoming.
• Estimating regional income indicators under transformations and access to limited population auxiliary information, with Tzavidis, N. and Würz, N., Journal of the Royal Statistical Society Series A, 2023, forthcoming.
• Iterative Kernel Density Estimation Applied to Grouped Data: Estimating Poverty and Inequality Indicators from the German Microcensus, with Walter, P.; Groß, M. and Weimer, K. Journal of Official Statistics, 38, pp. 599-635.
• Experimental UK regional consumer price inflation with model-based expenditure weights, with Dawber, J., Flower, T., Smith, P., Thomas, H., Tzavidis, N. and Würz, N., Journal of Official Statistics, 38, pp. 213-237.
• Intercensal updating using structure-preserving methods and satellite imagery, with Arias-Salazar, A., Koebe, T. and Rojas-Perilla, N., Journal of the Royal Statistical Society Series A, 2022, forthcoming.
• Kernel density smoothing of composite spatial data on administrative area level, with Erfurth, K., Groß, M. and Rendtel, U., AStA Wirtschafts- und Sozialstatistisches Archiv, 2021, forthcoming.
• Domain prediction with grouped income data, with Groß, M., Tzavidis, N. and Walter, P., Journal of the Royal Statistical Society: Series A, 2021, 184, pp. 1501-1523.
• The Fay–Herriot model for multiply imputed data with an application to regional wealth estimation in Germany, with Kreutzmann, A.-K., Marek, P., Runge, M. and Salvati, N., Journal of Applied Statistics, 2021, forthcoming.
• Data-Driven Transformations in Small Area Estimation, with Rojas-Perilla, N., Pannier, S. and Tzavidis, N., Journal of the Royal Statistical Society: Series A, 2020, 183, pp. 121-148.
• Smoothing and Benchmarking for Small Area Estimation, with Steorts, R. and Tzavidis, N., International Statistical Review, 2020, 88, pp.580-598.
• Switching between different non-hierarchical administrative areas via simulated geo-coordinates: A case study for student residents in Berlin, with Groß, M., Kreutzmann, A.-K., Rendtel, U. and Tzavidis, N., Journal of Official Statistics, 2020, 36, pp. 297-314.
• The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators, with Kreutzmann, A.-K., Pannier, S., Rojas-Perilla, N., Templ, M. and Tzavidis, N., Journal of Statistical Software, 2019, 91, pp. 1-33.
• Analysing radon accumulation in the home by flexible M-quantile mixed effect regression, with Borgoni, R., Carcagni, A. and Salvati, N., Stochastic Environmental Research and Risk Assessment, 2019, 33, pp. 375-394.
• The fayherriot command for estimating small-area indicators, with Halbmeier, C., Kreutzmann, A.-K. and Schröder, C., Stata Journal, 2019, 19, pp. 626-644.
• From start to finish: A framework for the production of small area official statistics, with Tzavidis, N., Zhang, L.-C., Luna Hernandez, A. and Rojas-Perilla, N., Journal of the Royal Statistical Society: Series A, Read paper, 2018, 181, pp. 927-979.
• Modelling the distribution of health related quality of life of advanced melanoma patients in a longitudinal multi-centre clinical trial using M-quantile random effects regression, with Borgoni, R., Del Bianco, P., Salvati, N., and Tzavidis, N., Statistical Methods in Medical Research, 2018, 27, pp. 549-563.
• Robust small area estimation under spatial non-stationarity, with C. Baldermann and N. Salvati, International Statistical Review, 2018, 86, pp. 136-159 .
• Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: Estimating literacy rates in Senegal, with Bruckschen, F., Salvati, N. and Zbiranski, T., Journal of the Royal Statistical Society: Series A, 2017, 180, pp. 1163-1190.
• Estimating the density of ethnic minorities and aged people in Berlin: Multivariate kernel density estimation applied to sensitive geo-referenced administrative data protected via measurement error, with Groß, M., Rendtel, U., T., Schmon and Tzavidis, N., Journal of the Royal Statistical Society: Series A, 2017, 180, pp. 161-183.
• Outlier robust small area estimation under spatial correlation, with Chambers, R., Münnich, R. and Tzavidis, N., Scandinavian Journal of Statistics, 2016, 43, pp. 806-826.
• Longitudinal analysis of the Strengths and Difficulties Questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression, with Flouri, E., Midouhas, E., Salvati, N. and Tzavidis, N., Journal of the Royal Statistical Society: Series A, 2016, 179, pp. 427-452.
• Simulation Tools for Small Area Estimation: Introducing the R-Package saeSim, with Warnholz, S., Austrian Journal of Statistics, 2016, 45, pp. 55-69.
• Spatial robust small area estimation, with Münnich, R., Statistical Papers, 2014, 55, pp. 653-670.