Ver?ffentlichungen

Ausgew?hlte Artikel in referierten Fachzeitschriften

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) 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.
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    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.

     

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