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Publikationen

Veröffentlichungen / Selected Publications:

  • J.P. Burgard, M.D. Esteban, D. Morales, and A. Pérez. Small area estimation under a measurement error bivariate Fay–Herriot model. Statistical Methods & Applications, 30:79–108, 2021.

  • J.P. Burgard, J. Krause, D. Kreber, and D. Morales. The generalized equivalence of regularizationand min-max robustification in linear mixed models. Statistical Papers, 2021. doi:10.1007/s00362-020-01214-z.

  • J.P. Burgard, J. Krause, and S. Schmaus. Estimation of regional transition probabilities for spatial dynamic microsimulations from survey data lacking in regional detail. Computational Statistics & Data Analysis, 154:107048, 2021.

  • J.P. Burgard. Neural networks and statistical learning. SIAM Review, 62(4):988–990, 2020.

  • J.P. Burgard, H. Dieckmann, J. Krause, H. Merkle, R. Münnich, K.M. Neufang, and S. Schmaus. A generic business process model for conducting microsimulation studies. Statistics in Transition New Series, 21(4):191–211, 2020.

  • J.P. Burgard, M.D. Esteban, D. Morales, and A. Pérez. A fay–herriot model when auxiliary variables are measured with error. TEST, 29(1):166–195, 2020.

  • J.P. Burgard, J. Krause, and R. Münnich. An elastic net penalized small area model combining unit- and area-level data for regional hypertension prevalence estimation. Journal of Applied Statistics, 2020. doi:10.1080/02664763.2020.1765323.

  • J.P. Burgard, R. Münnich, and M. Rupp. Qualitätszielfunktionen für stark variierende Gemeindegrößen im Zensus 2021. AStA Wirtschafts- und Sozialstatistisches Archiv, 14:5–65, 2020.

  • S. Zins and J.P. Burgard. Considering interviewer and design effects when planning sample sizes. Survey Methodology, 46(1):93–119, 2020.

  • "Data-driven Transformations in Small Area Estimation, N. Rojas mit S. Pannier, T. Schmid und N. Tzavidis, 2020, Journal of the Royal Statistical Society: Series A (Statistics in Society)

  • S. Bleninger, M. Fürnrohr, H. Kiesl, W. Krämer, H. Küchenhoff, J.P. Burgard, R. Münnich, and M. Rupp. Kommentare und Erwiderung zu: Qualitätszielfunktionen für stark variierende Gemeindegrößen im Zensus 2021. AStA Wirtschafts- und Sozialstatistisches Archiv, 14:67–98, 2019.

  • J. Breitkreuz, G. Brückner, J.P. Burgard, J. Krause, R. Münnich, H. Schröder, and K. Schüs- sel. Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Typ-2-Diabetes. AStA Wirtschafts-und Sozialstatistisches Archiv, 13(1):35–72, 2019.

  • R. Münnich, J.P. Burgard, and J. Krause. Adjusting selection bias in german health insurance records for regional prevalence estimation. Population Health Metrics, 17(13), 2019.

  • „The R Package emdi for Estimating and Mapping Regionally Disaggregated Indicators“, N. Rojas mit A.K. Kreutzmann, S. Pannier, T. Schmid, N. Tzavidis und M. Templ, 2019, Journal of Statistical Software 

  • J.P. Burgard, M. Neuenkirch, and M. Nöckel. State-dependent transmission of monetary policy in the euro area. Journal of Money, Credit and Banking, 2018. doi:10.1111/jmcb.12592.

  • A.-L. Wölwer, M. Breßlein, and J.P. Burgard. Gravity models in R. Austrian Journal of Statistics, 47(4):16–35, 2018.

  • A.-L. Wölwer, J.P. Burgard, J. Kunst, and M. Vargas. Gravity: Estimation methods for gravity models in R. Journal of Open Source Software, 3(31):1038, 2018.

  • „The Use of Data-driven Transformations and Their Applicability in Small Area Estimation“, 2018, Ph. D. thesis N. Rojas

  • J.P. Burgard, J.-P. Kolb, H. Merkle, and R. Münnich. Synthetic data for open and reprodu- cible methodological research in social sciences and official statistics. AStA Wirtschafts- und Sozialstatistisches Archiv, 11(3):233–244, 2017.

  • T. Singh, R. Laub, J.P. Burgard, and C. Frings. Disentangling inhibition-based and retrieval-based aftereffects of distractors: Cognitive versus motor processes. Journal of experimental psychology. Human perception and performance, 44(5):797–805, 2017.

Forschungsschwerpunkt Statistik und Ökonometrie