To promote outstanding young scientists, the Executive Board of the German Statistical Society announces the Wolfgang Wetzel Awards of the DStatG for the year 2020. The prize is named after the former Chairman of the DStatG, who during his tenure was particularly committed to strengthening mathematical-statistical methods in society. It is to be awarded during the Statistical Week. The prize is endowed with 1,000 euros. On February 18th, 2011, the Executive Board of the German Statistical Society decided to award the Wolfgang Wetzel Prize annually. As in 2011, the prize money in 2019 will be covered by Opens external link in new window Springer Spektrum | Springer-Verlag GmbH. We would like to thank Springer-Verlag for funding the Wolfgang Wetzel Prize.
The prize is aimed at young scientists up to a maximum of five years after their doctorate. The prize is awarded for an outstanding contribution to statistical methodology and its application. The contribution to be awarded usually consists of a publication already published or at least accepted for publication. It is also possible to award several publications written with different co-authors. In the case of co-authorship, the award may also be shared.
The publication (or publications) must have been published or accepted for publication in the year of the award or in the two preceding calendar years. The nomination is made by a member of the German Statistical Society. The proposed young scientist does not have to be a member of the DStatG. Self-proposals are not possible.
The prize will be awarded during the Statistical Week 2020 in Dresden. This year, the prize committee consists of Harry Haupt, Ostap Okhrin, Yarema Okhrin and Timo Schmid.
|2022||Dr. Sven Klaaßen|
|2021||Prize was not awarded this year.|
|2020||Dr. Miriam Isabel Seifert||Klüppelberg, C. & Seifert, M. (2020). Explicit results on conditional distributions of generalized exponential mixtures. Journal of Applied Probability, 57(3), 760-774. (https://doi.org/10.1017/jpr.2020.26)|
|2019||Prof. Nestor Parolya||Bodnar, T., Dette, H. & Parolya, N. (2019). Testing for Independence of Large Dimensional Vectors. The Annals of Statistics, 47(5), 2977 – 3008. (https://doi.org/10.1214/18-AOS1771)|
|2018||Dr. Yannick Hoga||Hoga, Y. (2019). Confidence Intervals for Conditional Tail Risk Measures in ARMA–GARCH Models. Journal of Business & Economic Statistics, 37(4), 613-624. (https://doi.org/10.1080/07350015.2017.1401543)|
|2017||Dr. Roxana Halbleib||Calzolari, G. & Halbleib, R. (2018). Estimating stable latent factor models by indirect inference. Journal of Econometrics, 205(1), 280-301. (https://doi.org/10.1016/j.jeconom.2018.03.014/a>)||more|
|2016||Dr. Heiko Grönitz||Groenitz, H. (2016). A covariate nonrandomized response model for multicategorical sensitive variables. Computational Statistics & Data Analysis, 103, 124-138. (https://doi.org/10.1016/j.csda.2016.04.007)|
|2015||Dr. Nadja Klein||Klein, N., Kneib, T. & Lang, S. (2015). Bayesian Generalized Additive Models for Location, Scale and Shape for Zero-Inflated and Overdispersed Count Data. Journal of the American Statistical Association, 110(509), 405-419. (https://doi.org/10.1080/01621459.2014.912955)||more|
|2014||JProf. Dr. Hans Manner||Grothe, O., Korniichuk, V. & Manner, H. (2014). Modeling Multivariate Extreme Events Using Self-Exciting Point Processes. Journal of Econometrics, 182(2), 269-289. (https://doi.org/10.1016/j.jeconom.2014.03.011)||more|
|2013||JProf. Dr. Dominik Wied||Rothe, C. & Wied, D. (2013). Misspecification Testing in a Class of Conditional Distributional Model. Journal of the American Statistical Association, 108(501), 314-324. (https://doi.org/10.1080/01621459.2012.736903)|
|2012||Dr. Sonja Greven||Greven, S., Dominici, F. & Zeger, S. (2011) An approach to the estimation of chronic air pollution effects using spatio-temporal information. Journal of the American Statistical Association 106(494), pp. 396-406 (https://doi.org/10.1198/jasa.2011.ap09392)|
|2011||Dr. Jörg Drechsler||Drechsler, J. & Reiter, J. P. (2010): Sampling with synthesis: a new approach for releasing public use census microdata. Journal of the American Statistical Association, Vol. 105, No. 492, S. 1347-1357. (https://doi.org/10.1198/jasa.2010.ap09480)|