Research Seminar

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Autumn Semester 2009

Date / Time

Speaker

Title

Location

11 September 2009
15:15-16:15

Nicolai Meinshausen
University of Oxford, Oxford UK

Event Details

Research Seminar in Statistics

Title	Trees, Forests and Group aggregation
Speaker, Affiliation	Nicolai Meinshausen, University of Oxford, Oxford UK
Date, Time	11 September 2009, 15:15-16:15
Location	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

Event Details

Research Seminar in Statistics

Title	Rumours and epidemics on random networks
Speaker, Affiliation	Valerie Isham, University College London UK
Date, Time	18 September 2009, 15:15-16:15
Location	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 October 2009
15:15-16:15

Jörg Rahnenführer
Technische Universität Dortmund

Event Details

Research Seminar in Statistics

Title	Statistical methods for estimating cancer progression from genetic measurements
Speaker, Affiliation	Jörg Rahnenführer, Technische Universität Dortmund
Date, Time	2 October 2009, 15:15-16:15
Location	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 October 2009
15:15-16:15

Paul Fearnhead
Lancaster University UK

Event Details

Research Seminar in Statistics

Title	Sequential Importance Sampling for General Diffusions
Speaker, Affiliation	Paul Fearnhead, Lancaster University UK
Date, Time	23 October 2009, 15:15-16:15
Location	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

Event Details

Research Seminar in Statistics

Title	The Sequential Rejection Principle of Familywise Error Rate Control
Speaker, Affiliation	Jelle Goeman, University Leiden
Date, Time	13 November 2009, 15:15-16:15
Location	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 December 2009
15:15-16:00

Richard Samworth
Cambridge University, UK

Event Details

Research Seminar in Statistics

Title	Maximum likelihood estimation of a multidimensional log-concave density
Speaker, Affiliation	Richard Samworth, Cambridge University, UK
Date, Time	4 December 2009, 15:15-16:00
Location	HG G 19.1
Assets	Abstractfile_download

Maximum likelihood estimation of a multidimensional log-concave densityread_more

HG G 19.1

* 4 December 2009
16:20-17:05

Ya'acov Ritov
Hebrew University, Jerusalem

Event Details

Research Seminar in Statistics

Title	A map to nowhere
Speaker, Affiliation	Ya'acov Ritov, Hebrew University, Jerusalem
Date, Time	4 December 2009, 16:20-17:05
Location	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

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.

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

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