Ver?ffentlichungen
Ausgew?hlte 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 Masterstudieng?ngen am Fachbereich Wirtschaftswissenschaft der Freien Universit?t 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.
- A?mann, C.; Gaasch, JC.; Stingl, D. (2022): A Bayesian Approach Towards Missing Covariate Data in Multilevel Latent Regression Models, Psychometrika.
- Walter, P.; Gro?, 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.; Gro?, 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.; Gro?, 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.
- Gro?, 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.; Schr?der, 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.
- Gro?, 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.
Ausgew?hlte Beitr?ge in Sammelb?nden und Büchern
- Meinfelder, F. (2023): Statistische Analyse Unvollst?ndiger 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.