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PD Dr. Jan Pablo Burgard
Research interests
- Data Science
- Survey Statistics
- Computational Statistics
Scientific career
| 2022 | Visiting professorship in applied statistics, Freie Universität Berlin |
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| 2019 | Habilitation in Statistics and Econometrics with the habilitation lecture: Gefahren und Chancen bei der Nutzung von Big Data in den Wirtschafts- und Sozialwissenschaften |
| 2018 | Promotion to Senior Academic Councilor |
| 2013 | Academic Councilor for lifetime |
| 2013 | Dr. rer. pol., Evaluation of Small Area Techniques for Applications in Official Statistics |
| 2009 | Diploma in Economics |
Awards and honors
| 2014 | Best paper delivered by a junior scientist during the SAE 2014 conference, Conference on Small Area Estimation 2014, Poznan, Polen. Charlotte Articus and Jan Pablo Burgard for the paper A Finite Mixture Fay Herriot-type model for the Estimation of Regional Rents |
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| 2014 | Förderpreis für die Dissertation, sponsored by Volksbank Trier. |
| 2011 | Joseph A. Schumpeter-Preis, sponsored by Deutsche Bundesbank (Headquarters in Rheinland-Pfalz and Saarland). For outstanding young researchers |
| 2003 | Walter Zechlin-Preis, sponsored by Rotary Club Lüneburg. For outstanding achievements in the social sciences and for exceptional social engagement in the area of student representation and student self-governance |
Research Projects
| 2022 - 2025 | Optimierte Strategien zur Kontrolle von Epidemien in hochgradig heterogenen Populationen (OptimAgent, Teilprojekt 4), BMBF. |
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| 2021 - 2025 | Algorithmic Optimization (ALOP), Verlängerung DFG-Graduiertenkolleg GRK2126. |
| 2017-2021 | Nikolaus Koch Stiftung Trier: Regionale Mikrosimulationen und Indikatorensysteme (REMIKIS) Principal Investigator: Ralf Münnich, Jan Pablo Burgard |
| 2016-2020 | DFG-Graduiertenkolleg GRK2126: Algorithmic Optimization (ALOP) |
| 2011-2013 | Nikolaus Koch Stiftung Trier: eLearning Infrastructure and Teaching Environment (eLITE) |
| 2015-2019 | Statistisches Bundesamt (DESTATIS): Research Innovation for Official and Survey Statistics (RIFOSS) |
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| 2015-2016 | Wissenschaftliches Institut der AOK (WidO): Schätzung von Krankheitsprävalenzen auf nicht repräsentativer Datenbasis |
| 2013-2014 | Statistisches Bundesamt (DESTATIS): Multiple Imputation beim Zensus |
| 2012-2013 | Statistisches Bundesamt (DESTATIS): Validierungsprojekt zum Stichprobenforschungsprojekt |
| 2007-2010 | Statistisches Bundesamt (DESTATIS): Stichprobenforschungsprojekt zum registergestützten Zensus 2011 (Zensus 2011) |
| 2013-2017 | EU FP7-INFRASTRUCTURES-2012-1-RTD-312691-INGRID Research Infrastructure: Inclusive Growth Research Infrastructure Diffusion (InGRID) |
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| 2012-2013 | Institut national de la statistique et des études économiques du Grand-Duché du Luxembourg (STATEC): Gewichtung des Labour Force Survey (LFS) in Luxemburg |
| 2010-2013 | EU FP7-SSH-2009-6-BLUE-ETS: Enterprise and Trade Statistics (BLUE-ETS) |
| 2009-2012 | Schweizerisches Bundesamt für Statistik (BFS): Simulation der Strukturerhebung und Kleingebiet-Schätzung zur schweizerischen Volkszählung (Schweizer Zensus) |
| 2008-2011 | EU FP7-SSH-2007-1-217322-AMELI: Advanced Methodology for European Laeken Indicators (AMELI) |
Bibliography
Peer-reviewed articles
JP. Burgard and P. Dörr. Generalized linear mixed models with crossed effects and unit-specific survey weights. Journal of Computational and Graphical Statistics, 2022. doi:10.1080/10618600.2021.2001342.
JP. Burgard, J. Krause, and D. Morales. A measurement error Rao–Yu model for regional prevalence estimation over time using uncertain data obtained from dependent survey estimates. TEST, 31:204–234, 2022.
JP. Burgard, C. Moreira Costa, and M. Schmidt. Robustification of the k-means clustering problem and tailored decomposition methods: when more conservative means more accurate. Annals of Operations Research, 2022. doi:10.1007/s10479-022-04818-w.
J. Krause, JP. Burgard, and D. Morales. l2–penalized approximate likelihood inference in logit mixed models for regional prevalence estimation under covariate rank-deficiency. Metrika, 85:459– 489, 2022.
J. Krause, JP. Burgard, and D. Morales. Robust prediction of domain compositions from uncer- tain data using isometric logratio transformations in a penalized multivariate Fay–Herriot model. Statistica Neerlandica, 76(1):65–96, 2022.
D. Morales, J. Krause, and JP. Burgard. On the use of aggregate survey data for estimating regional major depressive disorder prevalence. Psychometrika, 87:344–368, 2022.
J. Bracher, D. Wolffram, J. Deuschel, K. Görgen, J. L. Ketterer, A. Ullrich, S. Abbott, M. V. Barbarossa, D. Bertsimas, S. Bhatia, M. Bodych, N. I. Bosse, JP. Burgard, and et. al. A pre- registered short-term forecasting study of COVID-19 in germany and poland during the second wave. Nature Communications, 12(1), 2021.
JP. Burgard, M.D. Esteban, D. Morales, and A. Pérez. Small area estimation under a measure- ment error bivariate Fay–Herriot model. Statistical Methods & Applications, 30:79–108, 2021.
JP. Burgard, J. Krause, D. Kreber, and D. Morales. The generalized equivalence of regularization and min–max robustification in linear mixed models. Statistical Papers, 62(6):2857–2883, 2021.
JP. 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, 48(9):1659–1674, 2021.
JP. Burgard, J. Krause, R. Münnich, and Domingo Morales. l2-penalized temporal logit-mixed models for the estimation of regional obesity prevalence over time. Statistical Methods in Medical Research, 30(7):1744–1768, 2021.
JP. 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.
JP. Burgard, D. Morales, and A-L. Wölwer. Small area estimation of socioeconomic indi- cators for sampled and unsampled domains. AStA Advances in Statistical Analysis, 2021. doi:10.1007/s10182-021-00426-4.
A. Konrad, JP. Burgard, and R. Münnich. A two-level greg estimator for consistent estimation in household surveys. International Statistical Review, 89(3):635–656, 2021.
L. Sembach, JP. Burgard, and V. Schulz. A riemannian newton trust-region method for fitting gaussian mixture models. Statistics and Computing, 32(1):8, 2021.
JP. Burgard. Neural networks and statistical learning. SIAM Review, 62(4):988–990, 2020.
JP. 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.
JP. 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.
JP. Burgard, R. Münnich, and M. Rupp. Qualitätszielfunktionen für stark variierende Gemeinde- größen im Zensus 2021. AStA Wirtschafts- und Sozialstatistisches Archiv, 14:5–65, 2020.
S. Zins and JP. 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, JP. 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. Schüssel. Schätzung kleinräumiger Krankheitshäufigkeiten für die deutsche Bevölkerung anhand von Routinedaten am Beispiel von Diabetes Typ 2. AStA Wirtschafts- und Sozialstatistisches Archiv, 2019. accepted.
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. Online first.
Anna-Lena Wölwer, Martin Breßlein, and Jan Pablo Burgard. Gravity Models in R. Austrian Journal of Statistics, 47(4):16–35, 2018.
Jan Pablo Burgard, Jan-Philipp Kolb, Hariolf Merkle, and Ralf Münnich. Synthetic data for open and reproducible 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, 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. Computational 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.
Monographs
J.P. Burgard.Evaluation of Small Area Techniques for Applications in Official Statistics. Doktorarbeit, 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.
Book contributions
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 A. Steland, O. Okhrin, and E. Rafajłowicz, editors, Springer series Proceedings in Mathematics and Statistics. Springer, 2019. Accepted.
J.P. Burgard, R. Münnich, and T. Zimmermann. Impact of Sampling Designs in Small Area Estimation with Applications to Poverty Measurement, chapter 5, Seiten 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 Thomas Riede, Sabine Bechthold, and Notburga Ott, editors, Weiterentwicklung der amtlichen Haushaltsstatistiken, Seiten 101–111. SCIVERO, Berlin, 2013.
Invited lectures
J.P. Burgard and S. Schmaus. Uncertainty and uncertainty assessment in microsimulations. SMSA 2019, Dresden, Germany, 2019. Invited Presentation.
J. P. Burgard, M. D. Esteban Lefler, D. Morales, and A. Perez Martin. Estimating small area means using estimated covariates. SEIO2018 Oviedo, Spain, 2018. Invited Presentation.
J. P. Burgard, D. Morales, and A.-L. Woelwer. E910: Small area estimation with partially missing direct estimates. 11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018) Pisa, Italy, 2018. Invited Presentation.
J.P. Burgard and C. Articus. Concomitant Variable Mixture Models for Small Area Estimation: An Application to Estimating Regional ARPRs in Germany. SAE2017 Paris, France, 2017. Invited Presentation.
J.P. Burgard and S. Schmaus. Sensitivity Analysis for Demography-Based Microsimulation. OR Konferenz 2017 Berlin, Germany, 2017. Invited Presentation.
J.P. Burgard and R. Münnich. E-Learning und E-Klausuren am Lehrstuhl für Wirtschafts- und Sozialstatistik, Universität Trier. VHB-Arbeitstagung * Elektronische Prüfungen - Rückblick und Ausblick *, Göttingen, 2014.
R. Münnich and J.P. Burgard. Performance of Small Area Estimatiors under Selected Sampling Designs. 3rd International Conference of the ERCIM WG on COMPUTING & STATISTICS (ERCIM’10), 2010. Invited Presentation.