Veröffentlichungen

Ausgewählte Artikel in referierten Fachzeitschriften

Edochie, I.; Newhouse, D.; Tzavidis, N.; Schmid, T.; Foster, E.; Hernandez, A. L.; Ouedraogo, A.; Sanoh, A.; Savadogo, A. (2024): Small Area Estimation of Poverty in Four West African Countries by Integrating Survey and Geospatial Data, The Journal of Official Statistics, forthcoming.
Runge, M.; Schmid, T. (2023): Small Area with Multiply Imputed Survey Data, The Journal of Official Statistics, 39, pp. 507-533.
Harmening, S.; Kreutzmann, A.-K..; Schmidt, S.; Salvati, N.; Schmid, T. (2023): A Framework for Producing Small Area Estimates Based on Area-Level Models in R, The R Journal, 15, pp. 316-341.
Rendtel U.; Lee Y.; Gerks H. (2023): Eine Analyse des Studienerfolgs im Masterstudium auf der Basis von Umfrage- und administrativen Prüfungsdaten: Ein Vergleich von fünf Masterstudiengängen am Fachbereich Wirtschaftswissenschaft der Freien Universität Berlin, AStA Wirtschafts- und Sozialstatistisches Archiv, forthcoming.
Lee, Y.; Rojas-Perilla, N.; Runge, M.; Schmid, T. (2023): Variable selection using conditional AIC for linear mixed models with data-driven transformations, Statistics and Computing, forthcoming.
Krennmair, P.; Schmid, T. (2023): Flexible domain prediction using mixed effects random forests, Journal of the Royal Statistical Society Series C, forthcoming.
Würz, N.; Schmid, T.; Tzavidis, N. (2023): Estimating regional income indicators under transformations and access to limited population auxiliary information, Journal of the Royal Statistical Society Series A, forthcoming.
Hammon, A. (2022) Multiple imputation of ordinal missing not at random data, AStA Advances in Statistical Analysis, forthcoming.
Aßmann, C.; Gaasch, JC.; Stingl, D. (2022): A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models, Psychometrika.
Walter, P.; Groß, M.; Schmid, T.; Weimer, K. (2022): Iterative Kernel Density Estimation Applied to Grouped Data: Estimating Poverty and Inequality Indicators from the German Microcensus, Journal of Official Statistics, 38, pp. 599-635.
Dawber, J.; Würz, N.; Smith, P.; Flower, T.; Thomas, H.; Schmid, T.; Tzavidis, N. (2022): Experimental UK regional consumer price inflation with model-based expenditure weights, Journal of Official Statistics, 38, pp. 213-237.
Koebe, T.; Arias-Salazar, A.; Rojas-Perilla, N.; Schmid, T. (2022): Intercensal updating using structure-preserving methods and satellite imagery, Journal of the Royal Statistical Society Series A, 185 (Suppl. 2), pp. 170-196.
Erfurth, K.; Groß, M.; Rendtel, U.; Schmid, T. (2021): Kernel density smoothing of composite spatial data on administrative area level, AStA Wirtschafts- und Sozialstatistisches Archiv, forthcoming.
Walter, P.; Groß, M.; Schmid, T. and Tzavidis, N. (2021): Domain prediction with grouped income data, Journal of the Royal Statistical Society: Series A, 184, pp. 1501-1523.
Kreutzmann, A.-K.; Marek, P.; Runge, M.; Salvati, N.; Schmid, T. (2021): The Fay–Herriot model for multiply imputed data with an application to regional wealth estimation in Germany, Journal of Applied Statistics, forthcoming.
Rojas-Perilla, N.; Pannier, S.; Schmid, T.; Tzavidis, N. (2020): Data-Driven Transformations in Small Area Estimation, Journal of the Royal Statistical Society: Series A, 183, pp. 121-148.
Steorts, R.; Schmid, T.; Tzavidis, N. (2020): Smoothing and Benchmarking for Small Area Estimation, International Statistical Review, 88, pp. 580-598.
Groß, M.; Kreutzmann, A.-K.; Rendtel, U.; Schmid, T.; Tzavidis, N. (2020): Switching between different non-hierarchical administrative areas via simulated geo-coordinates: A case study for student residents in Berlin, Journal of Official Statistics, 36, pp. 297-314.
Hammon, A., & Zinn, S. (2020) Multiple imputation of binary multilevel missing not at random data, Journal of the Royal Statistical Society: Series C, 69, pp. 547-564.
Kreutzmann, A.-K.; Pannier, S.; Rojas-Perilla, N.; Schmid, T.; Templ, M.; Tzavidis, N. (2019): The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators, Journal of Statistical Software, 91, pp. 1-33.
Borgoni, R.; Carcagni, A.; Salvati, N.; Schmid, T. (2019): Analysing radon accumulation in the home by flexible M-quantile mixed effect regression, Stochastic Environmental Research and Risk Assessment, 33, pp. 375-394.
Halbmeier, C.; Kreutzmann, A.-K.; Schmid, T.; Schröder, C. (2019): The fayherriot command for estimating small-area indicators, Stata Journal, 19, pp. 626-644.
Tzavidis, N.; Zhang, L.-C.; Luna Hernandez, A.; Schmid, T.; Rojas-Perilla, N. (2018): From start to finish: A framework for the production of small area official statistics, Journal of the Royal Statistical Society: Series A, Read paper, 181, pp. 927-979.
Borgoni, R.; Del Bianco, P.; Salvati, N.; Schmid, T.; Tzavidis, N. (2018): 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, Statistical Methods in Medical Research, 27, pp. 549-563.
Baldermann, C.; Salvati, N.; Schmid, T. (2018): Robust small area estimation under spatial non-stationarity, International Statistical Review, 86, pp. 136-159 .
Schmid, T.; Bruckschen, F.; Salvati, N.; Zbiranski, T. (2017): Constructing socio-demographic indicators for National Statistical Institutes using mobile phone data: Estimating literacy rates in Senegal, Journal of the Royal Statistical Society: Series A, 180, pp. 1163-1190.
Groß, M.; Rendtel, U.; Schmid, T.; Schmon S.; Tzavidis, N. (2017): 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, Journal of the Royal Statistical Society: Series A, 180, pp. 161-183.
Schmid, T.; Tzavidis, N.; Münnich, R.; Chambers, R. (2016): Outlier robust small area estimation under spatial correlation, Scandinavian Journal of Statistics, 43, pp. 806-826.
Tzavidis, N.; Salvati, N.; Schmid, T.; Flouri, E.; Midouhas, E. (2016): Longitudinal analysis of the Strengths and Difficulties Questionnaire scores of the Millennium Cohort Study children in England using M-quantile random-effects regression, Journal of the Royal Statistical Society: Series A, 179, pp. 427-452.
Warnholz, S., & Schmid, T. (2016): Simulation Tools for Small Area Estimation: Introducing the R-Package saeSim, Austrian Journal of Statistics, 45, pp. 55-69.
Schmid, T., & Münnich, R. (2014): Spatial robust small area estimation, Statistical Papers, 55, pp. 653-670.

Ausgewählte Beiträge in Sammelbänden und Büchern

Meinfelder, F. (2023): Statistische Analyse Unvollständiger Daten. In: Moderne Verfahren der Angewandten Statistik, (eds. Gertheiss, J.; Schmid, M.; Spindler, M.), pp.1-39, Berlin, Springer.
Van den Brakel, J.; Smith P.; Elliott, D.; Krieg, S.; Schmid, T.; Tzavidis, N. (2021): Assessing Discontinuities and Rotation Group Bias in Rotating Panel Designs. In: Advances in Longitudinal Survey Methodology (ed. P. Lynn), pp. 399-423, Wiley.

Software

Harmening, S.; Kreutzmann, A.-K.; Pannier, S.; Rojas-Perilla, R.; Salvati, N.; Schmid, T.; Templ, M.; Tzavidis, N.; Würz, N. (2021): emdi – Estimating and Mapping Disaggregated Indicators, Version 2.0.2, https://cran.r-project.org/web/packages/emdi/index.html.
Warnholz, S., & Schmid, T. (2019): saeSim – Simulation Tools for Small Area Estimation, Version 0.10.0, https://cran.r-project.org/web/packages/saeSim/index.html.