ZüKoSt: Seminar on Applied Statistics

Would you like to be notified about these presentations via e-mail? Please subscribe here.

Modal title

Modal content

Spring Semester 2018

Date / Time

Speaker

Title

Location

4 May 2018
15:15-16:00

Marcel Wolbers
Roche AG

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	Design and analysis of a non-inferiority trial in a tropical disease
Speaker, Affiliation	Marcel Wolbers, Roche AG
Date, Time	4 May 2018, 15:15-16:00
Location	HG G 19.1
Abstract	We present the design and analysis of a randomized non-inferiority trial in talaromycosis, a major cause of human immunodeficiency virus (HIV)–related death in South and Southeast Asia. The talk will present the entire history of the trial highlighting the importance of a close collaboration between clinicians and statisticians. Moreover, it will focus on several statistical topics which arose during the trial including the choice of the non-inferiority margin, the issue of non-proportional hazards, and joint modeling of longitudinal fungal counts and mortality.

Design and analysis of a non-inferiority trial in a tropical disease read_more

HG G 19.1

17 May 2018
16:15-17:00

Nicholas G. Reich
University of Massachusetts, Amherst

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	Forecasting infectious disease epidemics via weighted density ensembles
Speaker, Affiliation	Nicholas G. Reich, University of Massachusetts, Amherst
Date, Time	17 May 2018, 16:15-17:00
Location	HG G 19.2
Abstract	Accurate and reliable predictions of infectious disease dynamics canbe valuable to public health organizations that plan inter-ventions to decrease or prevent disease transmission. Generally seen as the most robust type of predictive models, ensemble-based methodologies combine outputs from individual models to create a combined prediction for a target of interest. We have implemented ensemble methods that form a predictive density for a target of interest as a weighted sum of the predictive densities from component models. In the simplest case,equal weight is assigned to each component model; in more complex cases, the weights can vary with the location, prediction target, week of the season when the predictions are made, a measure of component model uncertainty, recent observations of disease incidence, and other observed covariates. In this talk, I will describe the methods used to estimate thecomponent model weights for these weighted density ensembles. For simple settings we use the degenerate EM algorithm, and in more complex settings we use gradient tree boosting to estimate penalized weights as functions of covariates. Additionally, I will describe our evaluation of these methods in two applications of forecasting influenza in the US. In one application, we combined 21 models from 4 different research groups to create real-time ensemble forecasts of influenza in the US in the 2017/2018 winter flu season.

Forecasting infectious disease epidemics via weighted density ensemblesread_more

HG G 19.2

25 May 2018
15:15-16:00

Fabio Sigrist
Hochschule Luzern

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	Default prediction using a tree-boosted Tobit model
Speaker, Affiliation	Fabio Sigrist, Hochschule Luzern
Date, Time	25 May 2018, 15:15-16:00
Location	HG G 19.1
Abstract	We consider the task of predicting whether loans are paid back or not. An often encountered problem in default prediction is the fact that there is relatively little default data since bankruptcies are usually uncommon events. We show how this issue can be alleviated by using a tree-boosted Tobit model in cases where there is additional data for the non-default events that is related to the default mechanism. Such additional data can consist of, for instance, number of days of delay by which loans were paid back, stock returns, or distance to default measures. We apply our proposed model for predicting defaults on loans made to Swiss small and medium-sized enterprises and obtain a large improvement in predictive performance compared to other state-of-the-art approaches.

Default prediction using a tree-boosted Tobit modelread_more

HG G 19.1

1 June 2018
15:15-16:00

Stephan Huckemann
Institut für Mathematische Stochastik, Göttingen

Event Details

ZüKoSt Zürcher Kolloquium über Statistik

Title	Some Statistics for Fingerprint Recognition
Speaker, Affiliation	Stephan Huckemann, Institut für Mathematische Stochastik, Göttingen
Date, Time	1 June 2018, 15:15-16:00
Location	HG G 19.1
Abstract	Recognition of persons by their fingerprints is rather ubiquitous: be it for unlocking smartphones, border control and for forensic applications, or for access to medical services, as for example in India. Challenges are manifold: handling bad quality, partial observations, tampered prints, distortions due to imprinting, as well as growth of children and juveniles. In this talk we discuss fingerprint features and statistical methods for their extraction, modeling, simulation, quality estimation and growth. Their foundations range from image analysis over shape analysis to complex analysis.

Some Statistics for Fingerprint Recognition read_more

HG G 19.1

Note: if you want you can subscribe to the iCal/ics Calender.

Archive: SS 24 AS 23 SS 23 AS 22 SS 22 AS 21 SS 20 AS 19 SS 19 AS 18 SS 18 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