Prof. Dr. Timo Schmid

Holder of the Chair of Statistics and Econometrics

Otto Friedrich University Bamberg
Faculty of Social and Economic Sciences
Room F21/00.76b
Feldkirchenstra?e 21
96045 Bamberg

Phone: 0951-863-2530

E-Mail

Consultation hours: by appointment

Curriculum Vitae(167.2 KB, 7 pages)
 

Research Fields

• Survey Statistics, especially Statistical Modelling
• Indices and Social Indicators, especially Poverty Measurement
• Use of Big Data (e. g. mobile phone data) in Statistics
• Small Area Estimation
• Simulation Techniques and Monte Carlo Methods
• Spatial Analysis Techniques
• Estimation of Wealth Using HFCS Data

Brief Biography

Timo Schmid has been head of the Chair of Statistics and Econometrics at Otto Friedrich University Bamberg and the Bamberg Centre for Empirical Studies (BACES) since 2021. Prior to his appointment at the University of Bamberg, Timo Schmid was responsible for the Chair of Applied Statistics at the Free University of Berlin and headed the statistical consultancy of the Free University of Berlin. Timo Schmid studied mathematics at the University of Tübingen and subsequently worked as a consultant at the management consultancy A.T. Kearney. From 2010 to 2012, Timo Schmid completed his doctorate at the Chair of Economic and Social Statistics at the University of Trier and was supported by a doctoral scholarship from the Foundation of German Business (Stiftung der Deutschen Wirtschaft).

Timo Schmid is a member of the Board of the German Statistical Society (DStatG) and editor of the journal Wirtschafts- und Sozialstatistisches Archiv of the DStatG.

Selected Publications

• Latent-Variable Modelling of Ordinal Outcomes in Language Data Analysis, with Krug, M., Leucht, A., Messer, P., S?nning, L. and Vetter, F., Journal of Quantitative Linguistics, 2024, 31(2), pp. 77-106.
• 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 Indicatos from the German Microcensus, with Walter, P.; Gro?, 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., Gro?, M. and Rendtel, U., AStA Wirtschafts- und Sozialstatistisches Archiv, 2021, forthcoming.
• Domain prediction with grouped income data, with Gro?, 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 Gro?, 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 Schr?der, 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 Gro?, 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.