Seminar for Statistics

Search

ZüKoSt: Seminar on Applied Statistics

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

×

Modal title

Modal content

Autumn Semester 2013

Date / Time Speaker Title Location
24 October 2013
16:15-17:00 		Lieven Clement
University of Gent
Details

ZueKoSt: Seminar on Applied Statistics

Title Wavelet based functional models for 'omics count profiles
Speaker, Affiliation Lieven Clement, University of Gent
Date, Time 24 October 2013, 16:15-17:00
Location HG G 19.1
Abstract The advent of next generation sequencing (seq) technology enables researchers for assessing genome-wide ‘omics profiles at an unpreceded resolution. The downstream statistical analysis is based on the number of sequenced reads mapping to the genomic regions of interest. The seq-technology conceptually allows for generating count profiles on a single nucleotide resolution. The majority of the algorithms for seq data either focus on segmentation or on differential analysis. But, they seldom perform both tasks simultaneously. Most statistical methods for differential analysis aggregate counts based on existing annotation. It implies that all reads mapping to unannotated regions are discarded. Improvements are possible by developing methods that (a) provide inference on a single base resolution and (b) perform segmentation, discovery and differential analysis, simultaneously. Within this context we explore the use of wavelet based functional models. We first introduce wavelets and show they can be used as building blocks in a functional model for count profiles. We will describe estimation and inference procedures. Finally, we illustrate our approach in a case study and show its potential for simultaneous discovery and differential analysis in sequencing studies.
Wavelet based functional models for 'omics count profilesread_more
HG G 19.1
* 31 October 2013
16:15-17:00 		John Copas
University of Warwick, UK
Details

ZueKoSt: Seminar on Applied Statistics

Title Correcting for outcome reporting bias in meta-analysis
Speaker, Affiliation John Copas, University of Warwick, UK
Date, Time 31 October 2013, 16:15-17:00
Location HG F 33.1
Abstract It is often suspected that outcomes in medical trials are selectively reported. A systematic review for a particular outcome of interest can only include trials where that outcome was reported, and may omit, for example, a trial which considers several outcome measures but only reports those giving significant results. Using information about studies considered in a systematic review but not included in the meta-analysis, I will describe a likelihood-based model for estimating the effect of outcome reporting bias on confidence intervals and p-values. Correcting for outcome reporting bias has the effect of moving estimated treatment effects towards the null value and hence more cautious assessments of significance. The bias can be very substantial, sometimes sufficient to completely overturn previous claims of significance. The seminar will be based on a forthcoming paper in Biostatistics: John Copas, Kerry Dwan, Jamie Kirkham and Paula Williamson (2014). A model–based correction for outcome reporting bias in meta-analysis. Biostatistics, to appear.
Correcting for outcome reporting bias in meta-analysisread_more
HG F 33.1
* 14 November 2013
16:15-17:00 		Werner Stahel
Seminar für Statistik, ETH Zürich
Details

ZueKoSt: Seminar on Applied Statistics

Title Das Wahre Modell ! ?
Speaker, Affiliation Werner Stahel, Seminar für Statistik, ETH Zürich
Date, Time 14 November 2013, 16:15-17:00
Location HG F 30
Abstract Statistik ist ein wichtiges Werkzeug für die empirischen Wissenschaften. Sobald deterministische Formeln nicht mehr reichen für die Beschreibung der Wirklichkeit, versucht man, die Gesetzmässigkeiten mit Modellen zu beschreiben, die den Zufall einbauen. Dafür sind statistische Regressionsmodelle grundlegend. Sie gehen von der Vorstellung aus, dass es Naturgesetze gibt, die allgemein gelten, und bauen darin zufällige Abweichungen ein, weil unsere Beobachtungen nicht perfekt sind. Auf der anderen Seite sind Modelle immer ''nur Modelle'', also unvollständige Beschreibungen der Wirklichkeit. Es gibt auch den pragmatischen Ansatz, der nicht die ''wahren Zusammenhänge'' abbilden will, sondern lediglich der ''Vorhersage'' dient: Wir wollen eine ''Zielgrösse'' so gut als möglich bestimmen, wenn wir die Werte von anderen Grössen, den ''Eingangsgrössen'', kennen, die mit ihr zusammenhängen. Bei der Entwicklung und bei der Anwendung von statistischen Regressionsmodellen pendeln wir zwischen einem pragmatischen Gebrauch und einer Interpretation als ''wahre Modelle''. Ich will im Vortrag anhand eines Beispiels und einigen allgemeinen Ueberlegungen zeigen, wie man mit diesem Dilemma umgehen kann.
Das Wahre Modell ! ?read_more
HG F 30
28 November 2013
16:15-17:00 		Danny Williamson
University of Exeter, UK
Details

ZueKoSt: Seminar on Applied Statistics

Title Identification and removal of structural biases in climate models, a statistical approach
Speaker, Affiliation Danny Williamson, University of Exeter, UK
Date, Time 28 November 2013, 16:15-17:00
Location HG G 19,1
Abstract Uncertainty quantification for climate models in the form of Bayesian calibration requires a detailed assessment of structural error or model discrepancy, that difference between the model and the reality it seeks to describe that is due to imperfect knowledge of the physics and compromises made for practical and computational reasons. However, many perceived structural biases in current climate models may not in fact be examples of this type of error, but may be due to non optimal choices of the model parameters. Given these perceptions, how are statisticians to work with climate scientists in order to elicit probabilistic judgements for model discrepancy? In this talk I will discuss a method for ruling out regions of parameter space that lead to poor representations of climate called history matching. I will compare history matching with Bayesian calibration and show that it can be used to remove current perceived structural biases, to identify the real sources of structural error and that it may therefore be a vital tool in tuning and climate model development. I will apply history matching to the fully coupled unflux adjusted atmosphere ocean generalised circulation model, HadCM3, and show that many perceived structural biases in ocean flows may not be structural at all.
Identification and removal of structural biases in climate models, a statistical approachread_more
HG G 19,1
5 December 2013
16:15-17:00 		Jon Wakefield
University of Washington
Details

ZueKoSt: Seminar on Applied Statistics

Title The Modeling of Pathogen-Specific Counts for Hand, Foot and Mouth Disease
Speaker, Affiliation Jon Wakefield, University of Washington
Date, Time 5 December 2013, 16:15-17:00
Location HG G 19.1
Abstract For many diseases, clinical illness can arise as the result of different genetic or viral pathogens. For example, cases of hand, foot and mouth disease (HFMD) arise due to different enteroviruses and can be clinically classified as either mild or severe. While clinical illness may be measured on the majority of a population, along with disease severity, the specific pathogen responsible will often be gathered on only a small subsample of individuals, sampled on the basis of disease severity. This talk have will have two halves. In the first half, we develop models for key transmission probabilities that allow an understanding of the transmission of the multiple pathogens in this non-random sampling setting. In the second half, we develop designs and inferential methods for community intervention vaccination trials in which direct, indirect, overall and total effects are estimated. Vaccines may target one pathogen, and we build on the modeling in the first half, since the vaccine efficacy measures are functions of the probabilities that are modeled within this aim. This is joint work with Leigh Fisher, Cici Chen and Steve Self.
The Modeling of Pathogen-Specific Counts for Hand, Foot and Mouth Diseaseread_more
HG G 19.1

Notes: events marked with an asterisk (*) indicate that the time and/or location are different from the usual time and/or location and if you want you can subscribe to the iCal/ics Calender.

Organisers: Peter Bühlmann, Reinhard Furrer, Leonhard Held, Markus Kalisch, Hans Rudolf Künsch, Marloes Maathuis, Martin Mächler, Lukas Meier, Nicolai Meinshausen, Mark D. Robinson, Carolin Strobl, Sara van de Geer

Archive: SS 25 AS 24 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

JavaScript has been disabled in your browser