ZüKoSt: Seminar on Applied Statistics

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Spring Semester 2011

Date / Time

Speaker

Title

Location

* 3 March 2011
16:15-17:45

Peter Bühlmann
Seminar für Statistik, ETH Zürich

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ZueKoSt: Seminar on Applied Statistics

Title	High-dimensional statistics: a 90 minutes tutorial
Speaker, Affiliation	Peter Bühlmann, Seminar für Statistik, ETH Zürich
Date, Time	3 March 2011, 16:15-17:45
Location	HG G 19.1
Abstract	This tutorial surveys methodology and aspects of theory for high-dimensional statistical inference when the number of variables or features greatly exceeds sample size. In the high-dimensional setting, major challenges include designing computational algorithms that are feasible for large-scale problems, assigning statistical error rates (e.g., p-values), and developing theoretical insights about the limits of what is possible. We will present some of the most important recent developments and discuss their implications for statistical practice.

High-dimensional statistics: a 90 minutes tutorialread_more

HG G 19.1

10 March 2011
16:15-17:30

Thomas Kneib
Universität Oldenburg

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ZueKoSt: Seminar on Applied Statistics

Title	Bayesian Geoadditive Sample Selection Models
Speaker, Affiliation	Thomas Kneib, Universität Oldenburg
Date, Time	10 March 2011, 16:15-17:30
Location	HG G 19.1
Abstract	Sample selection models attempt to correct for non-randomly selected data in a two-model hierarchy where, on the first level, a binary selection equation determines whether a particular observation will be available for the second level, i.e. in the outcome equation. Ignoring the non-random selection mechanism that is induced by the selection equation may result in biased estimation of the coefficients in the outcome equation. In the application that motivated this research, we analyse relief supply in earthquake-affected communities in Pakistan, where the decision to deliver goods represents the dependent variable in the selection equation whereas factors that determine the amount of goods supplied are analysed in the outcome equation. In this application, the inclusion of spatial effects is necessary since the available covariate information on the community level is rather scarce. Moreover, the high temporal dynamics underlying the immediate delivery of relief supply after a natural disaster calls for non-linear, time varying effects. We propose a geoadditive sample selection model that allows us to address these issues in a general Bayesian framework with inference being based on Markov chain Monte Carlo simulation techniques and apply it to the relief supply data from Pakistan.

Bayesian Geoadditive Sample Selection Modelsread_more

HG G 19.1

19 May 2011
16:15-17:30

Mario Fritz
MPI Saarbrücken

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ZueKoSt: Seminar on Applied Statistics

Title	Latent Additive Feature Models for Visual Recognition
Speaker, Affiliation	Mario Fritz, MPI Saarbrücken
Date, Time	19 May 2011, 16:15-17:30
Location	HG G 19.1
Abstract	Visual recognition is one of the key technologies for future applications in robotics, surveillance, media retrieval, personal assistance, etc. Despite the dramatic progress over the last decade, many fundamental questions remain unanswered. In my talk I will elaborate on one of those questions which is how to best encode visual information in order to facilitate robust and scalable recognition. Recently, sparse coding approaches have shown superior performance in comparison to the predominant vector quantization paradigm. We have proposed a probabilistic version of such coding schemes in a bayesian setting. Based on Latent Dirichlet Allocation (LDA) we have presented a latent additive feature model that has shown state-of-the-art performance in visual category recognition and detection as well as treatment of transparent objects. Most recently, the approach has been extended in a hierarchical fashion in order to provide a joint inference scheme in a multi-layered representation. The talk will give a brief introduction to the research area, describe the outlined approach in detail and show its merits on real-world data.

Latent Additive Feature Models for Visual Recognitionread_more

HG G 19.1

* 31 May 2011
15:15-16:30

Steve Scott
Google

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ZueKoSt: Seminar on Applied Statistics

Title	A Modern Bayesian Look at the Multi-Armed Bandit
Speaker, Affiliation	Steve Scott, Google
Date, Time	31 May 2011, 15:15-16:30
Location	HG G 19.1
Abstract	A multi-armed bandit is a sequential experiment with the goal of accumulating the largest possible reward from a payoff distribution with unknown parameters that are learned through experimentation. This article describes a heuristic for managing multi-armed bandits called randomized probability matching, which randomly allocates observations to arms according the Bayesian posterior probability that each arm is optimal. Advances in Bayesian computation have made randomized probability matching easy to apply to virtually any payoff distribution. This flexibility frees the experimenter to work with payoff distributions that correspond to certain classical experimental designs that have the potential to outperform "optimal" sequential methods.

A Modern Bayesian Look at the Multi-Armed Banditread_more

HG G 19.1

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Organisers: Peter Bühlmann, Reinhard Furrer, Leonhard Held, Markus Kalisch, Hansruedi Künsch, Marloes Maathuis, Martin Mächler, Werner Stahel, Sara van de Geer

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