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E-Mail: susanne.raessler(at)uni-bamberg.de

Pillar 2 ‘Education and Social Inequality Across the Entire Life Course’

Professor Dr. Susanne Rässler is particularly interested in supervising doctoral students in the areas of multiple imputation, correction methods of item- and unit-nonresponse in surveys, missing data, statistical matching, data anonymisation for disclosure control, Markov chain Monte Carlo methods, and Bayesian statistics.

Professor Rässler's website at the Chair of Statistics and Econometrics

Dr. Susanne Rässler has been Professor of Statistics and Econometrics at the Otto-Friedrich-University of Bamberg since September 2007. In addition to her professorship position she is member of the Council for Social and Economic Data (RatSWD), member of the commission for the scientific consultancy of the official statistics and the Federal Government for the census 2001 (Census Commission). Susanne Rässler has published several books on survey sampling, data fusion and statistical matching, and prediction techniques. In her journal articles her research focuses on methods for handling missing data, multiple imputation, data fusion, Bayesian methods, as well as matching techniques for causal analysis, sample theory and estimation of economic productivity on enterprise level.

Books

Rässler, S. (2002). Statistical Matching: A Frequentist Theory, Practical Applications, and Alternative Bayesian Approaches (Lecture Notes in Statistics, 168). New York: Springer.


Articles and Book Chapters

Gartner, H., Jensen, U. and Rässler, S. (2010). ‘Estimating German Overqualification with Stochastic Earnings Frontiers,’ AStA Advances in Statistical Analysis 94(1), pp. 33 – 51.

Drechsler, J., Bender, S. and Rässler, S. (2008). ‘Comparing Fully and Partially Synthetic Datasets for Statistical Disclosure Control in the German IAB Establishment Panel,’ Transactions on Data Privacy 1(3), pp. 105 - 130.

Rässler, S., Rubin, D.B. and Zell, E.R. (2008). ‘Incomplete Data in Epidemiology and Medical Statistics. In: Rao, C.R., Miller, J.P. and Rao, D.C. (eds.). Handbook of Statistics 27: Epidemiology and Medical Statistics. New York: Elsevier, pp. 569-601.

Rässler, S., Rubin, D.B. and Schenker, N. (2008). ‘Incomplete Data: Diagnosis, Imputation, and Estimation.’ In: de Leeuw, E.D., Hox, J.J. and Dillman, D.A. (eds.), International Handbook of Survey Methodology. New York: Lawrence Erlbaum Associates, pp. 370-386.

Rässler, S. (2006). ‘Der Einsatz von Missing Data Techniken in der Arbeitsmarktforschung des IAB,’ Allgemeines Statistisches Archiv 90(4), pp. 527-552.

Münnich, R. and Rässler, S. (2005). ‘PRIMA: A new Multiple Imputation Procedure for Binary Variables,’ Journal of Official Statistics 21(2), pp. 325-341.

Rässler, S. (2003). ‘A Non-Iterative Bayesian Approach to Statistical Matching,’ Statistica Neerlandica 57(1), pp. 58-74.

Kölling, A. and Rässler, S. (2003). ‘Die Einflüsse von Antwortverweigerung und mehrfacher Ergänzung fehlender Daten auf Produktivitätsschätzungen mit dem IAB-Betriebspanel,’ Jahrbücher für Nationalökonomie und Statistik 223/3, pp. 279-311.

Rässler, S. (2000). ‘Ergänzung fehlender Daten in Umfragen,’ Jahrbücher fürNationalökonomie und Statistik 220/1, pp. 64-94.