Navigation Area
 News

Events

Research seminars
 Algebraic geometry and moduli seminar
 Analysis seminar
 Fin & math doc seminar
 Geometry seminar
 ITS informal analytic number theory Seminar
 Number theory seminar
 Optimization and applications seminar
 Seminar on stochastic processes
 Statistics research seminar
 Symplectic geometry seminar
 Talks in financial and insurance mathematics
 Talks in mathematical physics
 Current Subcategory: ZüKoSt Zürcher Kolloquium über Statistik
 Zurich colloquium in applied and computational mathematics
 Zurich colloquium in mathematics
 Doctoral examinations
 Inaugural and farewell lectures
 Heinz Hopf Prize and Lectures
 Wolfgang Pauli Lectures
 Paul Bernays Lectures
 Conferences and workshops

Research seminars
 Awards and honours
ZüKoSt Zürcher Kolloquium über Statistik
Main content
Spring Semester 2017
Note: The highlighted event marks the next occurring event.
Date / Time  Speaker  Title  Location  

22 February 2017 16:1517:00 
Stef van Buuren Utrecht University Gerko Vink Utrecht University 
A quick tour with the mice package for imputing missing data  HG G 19.1  
Abstract: Nearly all data analytic procedures in R are designed for complete data and fail if the data contain NA's. Most procedures simply ignore any incomplete rows in the data, or use adhoc procedures like replacing NA with the "best value". However, such procedures for fixing NA's may introduce serious biases in the ensuing statistical analysis. Multiple imputation is a principled solution for this problem and is implemented in the R package MICE. In this talk we will give a compact overview of MICE capabilities for R experts, followed by a discussion.  
2 March 2017 16:1517:00 
Ben Marwick University of Washington, Seattle 
Reproducible Research Compendia via R packages  HG G 19.1  
Abstract: "Long considered an axiom of science, the reproducibility of scientific research has recently come under scrutiny after some highlypublicized failures to reproduce results. This has often been linked to the failure of the current model of journal publishing to provide enough details for reviewers to adequately assess the correctness of papers submitted for publication. One early proposal for ameliorating this situation is to bundle the different files that make up a research result into a publiclyavailable 'compendium'. At the time it was originally proposed, creating a compendium was a complex process. In this talk I show how modern software tools and services have substantially lightened the burden of making compendia. I describe current approaches to making these compendia to accompany journal articles. Several recent projects of varying sizes are briefly presented to show how my colleagues and I are using R and related tools (e.g. version control, continuous integration, containers, repositories) to make compendia for our publications. I explain how these approaches, which we believe to be widely applicable to many types of research work, subvert the constraints of the typical journal article, and improve the efficiency and reproducibility of our research."  
6 April 2017 16:1517:00 
Sebastian Engelke EPFL Lausanne 
Models for extremes on graphs  HG G 19.1  
Abstract: Maxstable processes are suitable models for extreme events that exhibit spatial dependencies. The dependence measure is usually a function of Euclidean distance between two locations. In this talk, we explore two models for extreme events on an underlying graphical structure. Dependence is more complex in this case as it can no longer be explained by classical geostatistical tools. The first model concentrates on river discharges on a network in the upper Danube catchment, where flooding regularly causes huge damage. Inspired by the work by Ver Hoef and Peterson (2010) for nonextreme data, we introduce a maxstable process on the river network that allows flexible modeling of flood events and that enables risk assessment even at locations without a gauging station. The second approach studies conditional independence structures for threshold exceedances, which result in a factorization of the likelihoods of extreme events. This allows for the construction of parsimonious dependence models that respect the underlying graph.  
27 April 2017 16:1517:00 
Marjolein Fokkema Department of Methods and Statistics der Universität Leiden, NL 
Prediction rule ensembles, or a Japanese gardening approach to random forests  HG G 19.1  
Abstract: Most statistical prediction methods provide a tradeoff between accuracy and interpretability. For example, single classification trees may be easy to interpret, but likely provide lower predictive accuracy than many other methods. Random forests, on the other hand, may provide much better accuracy, but are more difficult to interpret, sometimes even termed black boxes. Prediction rule ensembles (PREs) aim to strike a balance between accuracy and interpretability. They generally consist of only a small set of prediction rules, which in turn can be depicted as very simple decision trees, which are easy to interpret and apply. Friedman and Popescu (2008) proposed an algorithm for deriving PREs, which derives a large initial ensemble of prediction rules from the nodes of CART trees and selects a sparse final ensemble by regularized regression of the outcome variable on the prediction rules. The R package ‘pre’ takes a similar approach to deriving PREs and offers several additional advantages. For example, it employs an unbiased tree induction algorithm, allows for a randomforest type approach to deriving prediction rules, and allows for plotting of the final ensemble. In this talk, I will introduce PRE methodology and package 'pre', illustrate with examples based on psychological research data, and discuss some future directions.  
11 May 2017 16:1517:00 
Alexandre Pintore Winton Capital Management 
An introduction to Winton and research in financial markets  HG G 19.2  
Abstract: In this presentation I will give an introduction on the work we do at Winton, and in particular describe some of the research challenges we face across the investment process, from data collection and analysis, to the forecasting of asset returns.  
18 May 2017 16:1517:00 
Philip O'Neill University of Nottingham 
Modelling and Bayesian inference for the Abakaliki smallpox data  HG G 19.1  
Abstract: In 1967, an outbreak of smallpox occurred in the Nigerian town of Abakaliki. The details were recorded in a World Health Organisation report, and the resulting data set has reappeared numerous times in the literature on infectious disease modelling. Surprisingly, in virtually all cases most of the available data are ignored. Moreover, the one previous analysis which does consider the full data set uses approximation methods to fit a stochastic transmission model. We present a new analysis which avoids such approximations, using data augmented Markov chain Monte Carlo methods. 
Archive: AS 17 SS 17 AS 16 SS 16 AS 15 SS 15 AS 14 SS 14 AS 13 SS 13 AS 12 SS 12 AS 11 SS 11 AS 10 SS 10 AS 09