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News
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News from the Emmy Noether Research Group
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Prof. Dr. Sonja Greven takes over the Chair of Statistics
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Paper "Inference for L2-Boosting" by Rügamer and Greven accepted for publication in Statistics and Computing
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Prof. Dr. Nadja Klein, receives funding from DFG’s Emmy Noether Programme
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Top Download: "Flexible Bayesian additive joint models with an application to type 1 diabetes research" by Köhler, Umlauf, Beyerlein, Winkler, Ziegler and Greven
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koehler_top20_certificate.pdf
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Prof. Sonja Greven Granted the Princess Therese of Bavaria Award
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Paper "Modelling Regional Patterns of Inefficiency" by Klein, Herwartz and Kneib accepted for publication in Journal of Econometrics
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Paper "Assessing the relationship between markers of glycemic control through flexible copula regression models" by Espasandin Dominguez, Cadarso-Suárez, Kneib, Klein, Radice, Lado-Baleato and Gude accepted
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Prof. Greven receives DFG funding for „Flexible regression methods for curve and shape data“
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Paper "Inference for L2-Boosting" by Rügamer and Greven appeared in Statistics and Computing 30(2)
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Paper "Comments on: Inference and computation with Generalized Additive Models and their extensions" by Greven and Scheipl appeared in TEST
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Paper "Boosting Functional Response Models for Location, Scale and Shape with an Application to Bacterial Competition" by Stöcker, Brockhaus, Schaffer, von Bronk, Opitz and Greven accepted
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New preprint "Selective Inference for Additive and Linear Mixed Models" by Rügamer, Baumann and Greven at arXiv
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Prof. Greven was selected for the Gumbel lecture 2020 of the German Statistical Society (DStatG)
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Paper "Boosting Functional Regression Models with FDboost" by Brockhaus, Rügamer and Greven has appeared in Journal of Statistical Software 94(10)
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New preprint "Predicting respondent difficulty in web surveys: A machine-learning approach based on mouse movement features" by Fernández-Fontelo, Kieslich, Henninger, Kreuter and Greven at arXiv
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Dr. Matthias Eckardt Granted a Walter Benjamin Fellowship of the DFG
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Paper "Predicting question difficulty in web surveys: A machine-learning approach based on mouse movement features" by Fernandez-Fontelo, Kieslich, Henninger, Kreuter and Greven will appear in Social Science Computer Review
