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Prof. Dr. rer. pol. Jan Pablo Burgard

Jan Pablo Burgard

Freie Universität Berlin

FB Wirtschaftswissenschaft

Institut für Statistik und Ökonometrie

Gast-Professor

Professor für Angewandte Statistik

Adresse
Garystr. 21
Raum 322
14195 Berlin
  • 2019 Habilitation: Venia Legendi in Statistik und Ökonometrie, Fachbereich IV, Universität Trier 
  • seit 2017 Senior Researcher, Research Innovation for Official and Survey Statistics (RIFOSS) Institut, Universität Trier. 
  • seit 2013 Akademischer Oberrat auf Lebenszeit, Professur für Wirtschafts- und Sozialstatistik, Prof. Dr. Ralf Münnich, Fachbereich IV, Universität Trier. 
  • 2013 Dr. rer. pol., Evaluation of Small Area Techniques for Applications in Official Statistics, Universität Trier 
  • 2007 – 2013 Wissenschaftlicher Mitarbeiter, Professur für Wirtschafts- und Sozialstatistik, Prof. Dr. Ralf Münnich, Fachbereich IV, Universität Trier. 
  • 2003 – 2009 Diplom-Volkswirt, Universität Trier.
  • Small Area Schätzung
  • Survey Statistik
  • Angewandte Statistik
  • Computationale Statsitik
  • Statistical Methods in Data Science

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.

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. 

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. 

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. 

R. Münnich, J.P. Burgard, S. Gabler, M. Ganninger, and J.-P. Kolb. Small area estimation in the german census 2011. Statistics in Transition New Series and Survey Methodology, 17(1):25 

– 40, 2016.
J.P. Burgard and R. Münnich. SAE teaching using simulations. Statistics in Transition New 

Series and Survey Methodology, 16(4):603–610, 2015. 

R. Münnich, S. Gabler, C. Bruch, J.P. Burgard, T. Enderle, J-P. Kolb, and T. Zimmermann. Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte. AStA Wirtschafts- und Sozialstatistisches Archiv, 9(3-4):269–304, 2015. 

J.P. Burgard, R. Münnich, and T. Zimmermann. The impact of sampling designs on small area estimates for business data. Journal of Official Statistics, 30(4):749–771, 2014. 

R. Münnich, J.P. Burgard, and M. Vogt. Small Area-Statistik: Methoden und Anwendungen. AStA Wirtschafts- und Sozialstatistisches Archiv, 6:149–191, 2013. 

J.P. Burgard and R. Münnich. Modelling over- and undercounts for design-based Monte Carlo studies in small area estimation: An application to the German register-assisted census. Compu- tational Statistics & Data Analysis, 56(10):2856–2863, 2012. 

R. Münnich and J.P. Burgard. On the influence of sampling design on small area estimates. Journal of the Indian Society of Agricultural Statistics., 66(1):145–156, 2012. Invited paper. 

R. Münnich, J.P. Burgard, B. Höfler-Hoang, J. Nicknig, and T. Zimmermann. Individualisiertes eLearning - Eine innovative Anwendung auf die statistische Grundausbildung an der Universität Trier. Hamburger eLearning Magazin, 7:51–52, 2011. 

R. Münnich, S. Gabler, M. Ganninger, J.P. Burgard, and J.-P. Kolb. Das Stichprobendesign des registergestützten Zensus. Methoden-Daten-Analysen, 1:37–61, 2011. 

E. Simoes, C. Emrich, S. Brucker, J.P. Burgard, T. Würfel, and R. Münnich. Gesundheitsstrategie Baden-Württemberg: Auf welche Datenbasis können regionale Gesundheitskonferenzen für die Ist- und Bedarfsanalysen zurückgreifen? Gesundheitswesen, 73(3):205–205, 2011. 

J.P. Burgard. Evaluation of small area techniques for applications in official statistics. Doktorar- beit, Universität Trier, 2013. 

R. Münnich, S. Gabler, M. Ganninger, J.P. Burgard, and J.-P. Kolb. Stichprobenoptimierung und Schätzung im Zensus 2011, Band 21 von Statistik und Wissenschaft. Statistisches Bundesamt, Wiesbaden, 2012. 

J.P. Burgard. Erstellung von Karteileichen- und Fehlbestandsmodellen durch Multilevel-Modelle. Diplomarbeit, Universität Trier, 2009. 

J.P. Burgard, J. Krause, H. Merkle, R. Münnich, and S. Schmaus. Dynamische Mikrosimulationen zur Analyse und Planung regionaler Versorgungsstrukturen in der Pflege. In Mikrosimulationen, pages 283–313. Springer, 2020. 

J.P. Burgard, R. Münnich, and T. Zimmermann. Impact of sampling designs in small area estimation with applications to poverty measurement, Chapter 5, pages 83–108. Wiley-Blackwell, 2016. 

R. Münnich, J.P. Burgard, and T. Zimmermann. Wie genau sind Kreisergebnisse des Mikrozensus – Einsatzmöglichkeiten von Small-Area-Verfahren. In T. Riede, S. Bechthold, and N. Ott, editors, Weiterentwicklung der amtlichen Haushaltsstatistiken, pages 101–111. SCIVERO, Berlin, 2013. 

J.P. Burgard, J. Krause, H. Merkle, R. Münnich, and S. Schmaus. Conducting a dynamic micro- simulation for care research: Data generation, transition probabilities and sensitivity analysis. In Ansgar Steland, Ewaryst Rafajłowicz, and Ostap Okhrin, editors, Stochastic Models, Statistics and Their Applications, pages 269–290. Springer International Publishing, 2019. 

P. Dörr and J.P. Burgard. Survey-weighted unit-level small area estimation. In A. Abbruzzo, E. Brentari, M. Chiodi, and D. Piacentino, editors, Book of Short Papers SIS 2018, page 689. Pearson, 2018. 

J.P. Burgard, R. Münnich, and T. Zimmermann. Impact of sampling on small area estimation in business surveys. In Proceedings of the ISI conference, Hong Kong, China, 2013. STS 049. 

J.P. Burgard, R. Münnich, and S. Zins. Measuring change of poverty estimates on small area level. In Proceedings of the ISI conference, Hong Kong, China, 2013. STS 039. 

J.P. Burgard, R. Münnich, and T. Zimmermann. Small area modelling under complex survey designs for business data. In Proceedings of the Fourth International Conference of Establishment Surveys (ICES IV), Montréal, Canada, 2012. http://www.amstat.org/meetings/ices/2012/ papers/301906.pdf. 

J.P. Burgard and R. Münnich. Using register information from multiple aggregation levels for the prediction of small area counts and means in the swiss structural survey. In Proceedings of the ISI conference, Dublin, Ireland, 2011. http://2011.isiproceedings.org/papers/950386.pdf. 

R. Münnich, S. Gabler, M. Ganninger, J.P. Burgard, and J.-P. Kolb. Optimal sampling design and estimation in the German Census. In ITACOSM invited paper, Pisa, Italy, 2011. 

S. Zins, J.P. Burgard, and R. Münnich. Measuring poverty and income inequality indicators and their evolution over time from complex survey samples. In Proceedings of the ISI conference, Dublin, Ireland, 2011. http://2011.isiproceedings.org/papers/950798.pdf. 

R. Münnich, J.P. Burgard, and M. Vogt. Small area estimation for population counts in the German Census 2011. In Joint Statistical Meeting, Proceedings of the Survey Research Methods Section, Invited paper session on recent advances in small area-statistics, Washing- ton D.C., USA, 2009. American Statistical Association. http://www.amstat.org/sections/ srms/proceedings/y2009/Files/302887.pdf.


J.P. Burgard and T. Kranz. Temporary settlement of payments: Monetäres Substitut zur Auf- rechterhaltung von Geldströmen, 2020. Trier University, Research Papers in Economics No. 05/20. 

J.P. Burgard, J. Krause, and D. Kreber. Regularized area-level modelling for robust small area estimation in the presence of unknown covariate measurement errors, 2019. Trier University, Research Papers in Economics No. 4/19. 

J.P. Burgard, J. Krause, and R. Münnich. Penalized small area models for the combination of unit-and area-level data, 2019. Trier University, Research Papers in Economics No. 5/19. 

J.P. Burgard, R. Münnich, and M. Rupp. A generalized calibration approach ensuring coherent estimates with small area constraints, 2019. Trier University, Research Papers in Economics No. 10/19. 

P. Dörr and J.P. Burgard. Data-driven transformations and survey-weighting for linear mixed models, 2019. Trier University, Research Papers in Economics No. 16/19. 

Forschungsschwerpunkt Statistik und Ökonometrie