Departement Mathematik

Statistics research seminar

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Herbstsemester 2009

Datum / Zeit Referent:in Titel Ort
11. September 2009
15:15-16:15 		Nicolai Meinshausen
University of Oxford, Oxford UK
Details

Research Seminar in Statistics

Titel Trees, Forests and Group aggregation
Referent:in, Affiliation Nicolai Meinshausen, University of Oxford, Oxford UK
Datum, Zeit 11. September 2009, 15:15-16:15
Ort HG G 19.1
Abstract In higher-dimensional regression and classification, there is a natural tradeoff between simplicity of the algorithm and its predictive power. Simple procedure like trees are intuitive to understand, yet they are clearly beaten in terms of predictive accuracy by more complex methods like tree ensembles, including Random Forests. I will share some thoughts and notes on this and show that a convex relaxation to an optimal partitioning of the data yields a new algorithm, which I call group aggregation. Predictions are simply weighted averages over empirical group means, for suitably selected groups of observations. The group selection amounts to a quadratic programming problem and can be solved effciently. Even though the algorithm contains no explicit tuning parameters in the most simple version, group aggregation gets close to Random Forests predictive power, while maintaining or surpassing the simplicity of trees. An application to emulation in climate modeling will be discussed.
Trees, Forests and Group aggregationread_more
HG G 19.1
18. September 2009
15:15-16:15 		Valerie Isham
University College London UK
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Research Seminar in Statistics

Titel Rumours and epidemics on random networks
Referent:in, Affiliation Valerie Isham, University College London UK
Datum, Zeit 18. September 2009, 15:15-16:15
Ort HG G 19.1
Abstract The Susceptible-Infected-Removed (SIR) epidemic model is a fundamental model for the spread of infection in a homogeneously-mixing population. It is a special case of a more general stochastic rumour model in which there is an extra interaction. Thus, not only does an ignorant (susceptible) contacted by a spreader (infective) become a spreader, and spreaders may "forget" the rumour and become stiflers (removals), but also spreaders may become stiflers if they attempt to spread the rumour to a spreader or stifler (who will have already heard it). For both epidemics and rumours, there is particular interest in using a random network to represent population structure, with applications to the spread of infection or information on social networks. The talk will discuss a) the effect of the population size on thresholds for epidemic/rumour spread, and b) the effect of different network structures.
Rumours and epidemics on random networksread_more
HG G 19.1
2. Oktober 2009
15:15-16:15 		Jörg Rahnenführer
Technische Universität Dortmund
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Research Seminar in Statistics

Titel Statistical methods for estimating cancer progression from genetic measurements
Referent:in, Affiliation Jörg Rahnenführer, Technische Universität Dortmund
Datum, Zeit 2. Oktober 2009, 15:15-16:15
Ort HG G 19.1
Abstract Human tumors are often associated with typical genetic events like tumor-specific chromosomal alterations. The identification of characteristic pathogenic routes in such tumors can improve the prediction of (disease-free) survival times und thus helps in choosing the optimal therapy. In recent years we have developed a biostatistical model for estimating the most likely pathways of chromosomal alterations from cross-sectional data. In this model progression is described by the irreversible, typically sequential, accumulation of somatic changes in cancer cells. The model was validated both statistically and clinically in various ways. We have also introduced a method to determine the optimal number of tree components based on a new BIC criterion. The new model is characterized by a high level of interpretability. Further, it allows the introduction of a genetic progression score (GPS) that quantifies univariately the progression status of a disease. Progression of a single patient along such a model is typically correlated with increasingly poor prognosis. Using Cox regression models we could demonstrate that the GPS is a medically relevant prognostic factor that can be used to discriminate between patient subgroups with different expected clinical outcome. Both for prostate cancer patients and for patients with different types of brain tumors a higher GPS is correlated with shorter time to relapse or death. The clinical relevance of such a disease progression model depends on the stability of the statistical model estimation process and on the predictive power of the derived progression score regarding survival times. Simulation studies show that the topology of our model can not always be estimated precisely. We present a study for determining the necessary sample size for recovering a true relationship between genetic progression and disease-free survival times. All studies are performed with the new R package Rtreemix for the estimation of such progression models.
Statistical methods for estimating cancer progression from genetic measurementsread_more
HG G 19.1
23. Oktober 2009
15:15-16:15 		Paul Fearnhead
Lancaster University UK
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Research Seminar in Statistics

Titel Sequential Importance Sampling for General Diffusions
Referent:in, Affiliation Paul Fearnhead, Lancaster University UK
Datum, Zeit 23. Oktober 2009, 15:15-16:15
Ort HG G 19.1
Abstract We present a general approach for performing sequential importance sampling for general diffusion models. This method avoids any time-discretisation approximation, and thus enables unbiased estimates of expectations of functions of the diffusion. It can be derived by considering simple sequential importance samplers for discrete-time approximations to diffusions, together with the tricks of Rao-Blackwellisation and retrospective sampling. The approach is related to recent work on unbiased estimation (and perfect simulation) of diffusions, but extends considerably the class of diffusion models that can be considered. It is also related to work onunbiased estimation by Wagner(1989). The links to these previous works will be discussed.
Sequential Importance Sampling for General Diffusionsread_more
HG G 19.1
13. November 2009
15:15-16:15 		Jelle Goeman
University Leiden
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Research Seminar in Statistics

Titel The Sequential Rejection Principle of Familywise Error Rate Control
Referent:in, Affiliation Jelle Goeman, University Leiden
Datum, Zeit 13. November 2009, 15:15-16:15
Ort HG G 19.1
Abstract We present a general sequentially rejective multiple testing procedure for multiple hypothesis testing. Many well known familywise error (FWER) controlling methods can be constructed as special cases of this procedure, among which are the procedures of Holm, Shaffer and Hochberg, parallel and serial gatekeeping procedures, modern procedures for multiple testing in graphs, resampling based multiple testing procedures, and even the closed testing and partitioning procedures. It is possible to prove that sequentially rejective multiple testing procedures strongly control the FWER if they fulfill simple criteria of monotonicity of the critical values and weak FWER control in each single step. The sequential rejection principle thus gives a novel theoretical perspective on many well-known multiple testing procedures, emphasizing the sequential aspect. Its main practical usefulness is for the development of multiple testing procedures for null hypotheses, possibly logically related, that are structured in a graph. We illustrate the general procedure with many examples of graph-based and other procedures.
The Sequential Rejection Principle of Familywise Error Rate Controlread_more
HG G 19.1
4. Dezember 2009
15:15-16:00 		Richard Samworth
Cambridge University, UK
Details

Research Seminar in Statistics

Titel Maximum likelihood estimation of a multidimensional log-concave density
Referent:in, Affiliation Richard Samworth, Cambridge University, UK
Datum, Zeit 4. Dezember 2009, 15:15-16:00
Ort HG G 19.1
Unterlagen Abstractfile_download
Maximum likelihood estimation of a multidimensional log-concave densityread_more
HG G 19.1
* 4. Dezember 2009
16:20-17:05 		Ya'acov Ritov
Hebrew University, Jerusalem
Details

Research Seminar in Statistics

Titel A map to nowhere
Referent:in, Affiliation Ya'acov Ritov, Hebrew University, Jerusalem
Datum, Zeit 4. Dezember 2009, 16:20-17:05
Ort HG G 19.1
Abstract We consider the maximal a-posteriori path estimator of an HMM process. We show that this estimator may be unreasonable when the state space is non-finite, or the process is in continuous time. We argue that this sheds a doubt on the usefulness of the concept in the standard finite state space in discrete time HMM model. We will then discuss some results concerning the well behavior of the a-posteriori probability of the a state given the data.
A map to nowhereread_more
HG G 19.1

Hinweise: mit einem Stern gekennzeichnete Ereignisse (*) zeigen an, dass die Zeit und/oder der Ort von der üblichen Zeit und/oder dem üblichen Ort abweichen und wenn Sie möchten, können Sie den iCal/ics-Kalender abonnieren.

Organisatoren:innen: Andrew D. Barbour, Peter Bühlmann, Leonhard Held, Hans-Rudolf Künsch, Marloes Maathuis, Werner Stahel, Sara van de Geer

Archiv: HS 24 FS 24 HS 23 FS 23 HS 22 FS 22 HS 21 FS 20 HS 19 FS 19 HS 18 FS 18 HS 17 FS 17 HS 16 FS 16 HS 15 FS 15 HS 14 FS 14 HS 13 FS 13 HS 12 FS 12 HS 11 FS 11 HS 10 FS 10 HS 09

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