Plenary & Invited Speakers
Plenary Speakers
| Speaker | Presentation Title | University |
|---|---|---|
| Sara van de Geer | Adaptivity of signal priors | ETH Zurich, CH |
| Per Kragh Andersen | Measuring Expected Years Of Life Lost | University of Copenhagen, DK |
| Donald B. Rubin | Modern Computing Implementing Classical, But Heretofore Unnurtured Statistical Ideas | Tsinghua University, Beijing, China Temple University, Philadelphia, USA |
| Michael Jordan | Statistical Machine Learning: Dynamical, Economic and Stochastic Perspectives | University of California, Berkeley, USA |
Invited Speakers
| Speaker | Presentation Title | University |
|---|---|---|
| Giampiero Marra | Generalised Joint Regression Modelling | University College, London, UK |
| Davy Paindaveine | Halfspace depth for scatter matrices | Université Libre de Bruxelles, BE |
| Dietrich von Rosen | The Growth Curve model under high dimensions with applications to profile analysis | Swedish University of Agricultural Sciences, SE |
| James Wason | Novel designs for trials with multiple treatments and subgroups | Newcastle University, UK |
| Richard Samworth | Log-concave density estimation | University of Cambridge, UK |
| Axel Gandy | Some examples of handling uncertainty in industrial applications | Imperial College, London, UK |
| Christiane Baumeister | Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks | University of Notre Dame, USA |
| A.M. Robert Taylor | Detecting Regimes of Predictability in the U.S. Equity Premium | University of Essex, UK |
| Timo Schmid | Estimating socio-demographic indicators using mobile phone data with applications in Germany and Senegal | Freie Universität Berlin, DE |
| Friederike Paetz | Latent Class Analysis in Marketing: Drawing Inferences for Social Brand Personalities | TU Clausthal, DE |
| David Kaplan | An Approach to Addressing Multiple Imputation Model Uncertainty Using Bayesian Model Averaging | University of Wisconsin, USA |
| Mathieu Rosenbaum | How do market participants contribute to market quality? A statistical approach | Centre De Mathematiques Appliquees, FR |
| Miguel Hernán | Estimating per-protocol effects. Randomized trials analyzed like observational studies | Harvard T.H. Chan School of Public Health, USA |
| Benjamin Hofner | Statistical issues in drug development and the role of statisticians in regulatory agencies | PEI Langen, DE |
| Nanny Wermuth | How can graphical Markov models aid causal inference? | Chalmers University of Technology, Gothenburg, SE |
| Heike Hofmann | Visual Inference: leveraging the power of our eyes | Iowa State University, USA |
| Piet Daas | Using Big Data in Official Statistics | CBS, Heerlen, NL |
| Bettina Grün | Identifying Mixtures of Mixtures Using Bayesian Estimation | Johannes Kepler Universität Linz, AUT |
| Thomas P.A. Debray | Clinical Prediction Models and the role of Evidence Synthesis | UMC Utrecht, NL |
| Janine Illian | Point processes — abstraction and practical relevance in ecology | University of St Andrews, UK |
| Steffi Pohl | Using timing information to model missing values in test data | Freie Universität Berlin, DE |
| Pavel Krivitsky | Inference for Social Network Models from Egocentrically-Sampled Data | University of Wollongong, AUS |
| Pamela Shaw | Estimation methods to address correlated covariate and time-to-event error | University of Pennsylvania, USA |
| Malgorzata Bogdan | Convex optimization methods for identifying predictors when n<p | Wroclaw University of Science and Thechnology, PL |
| Joachim Buhmann | Robust algorithmics: a foundation for science?! | ETH Zurich, CH |
| Ingo Steinwart | Aspects of adaptive density-based cluster analysis | University of Stuttgart, DE |
| Günther Palm | Learning in artificial and real neural networks | University of Ulm, DE |
| Eyke Hüllermeier | Analyzing and Learning from Ranking Data: New Problems and Challenges | University of Paderborn, DE |
| Laura Martignon | Statistical Literacy and Statistical Education | PH Ludwigsburg, DE |
| Vanessa Didelez | Best Subset Selection: The Holy Grail for Variable Selection? | Leibniz Institute, University of Bremen, DE |