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Asymptotic Inference For Partially Observed Branching Processes

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Statistical Inference for Partially Observed Stochastic Epidemics

Andrea Kraus

This work is concerned with the estimation of the spreading potential of the disease in the initial stages of an epidemic. A speedy and accurate estimation is important for determining whether or not interventions are necessary to prevent a major outbreak. ...

EPFL2013

A Euclidean Likelihood Estimator for Bivariate Tail Dependence

Michal Warchol, Boris Oumow

The spectral measure plays a key role in the statistical modeling of multivariate extremes. Estimation of the spectral measure is a complex issue, given the need to obey a certain moment condition. We propose a Euclidean likelihood-based estimator for the ...

Taylor & Francis Inc2013

Bayesian inference from composite likelihoods, with an application to spatial extremes

Anthony Christopher Davison

Composite likelihoods are increasingly used in applications where the full likelihood is analytically unknown or computationally prohibitive. Although some frequentist properties of the maximum composite likelihood estimator are akin to those of the maximu ...

Academia Sinica, Institute of Statistical Science2012

Reconstruction of Biomedical Images and Sparse Stochastic Modeling

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We propose a novel statistical formulation of the image-reconstruction problem from noisy linear measurements. We derive an extended family of MAP estimators based on the theory of continuous-domain sparse stochastic processes. We highlight the crucial ro ...

IEEE2012

Reconstruction Of Biomedical Images And Sparse Stochastic Modeling

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

Ieee2012

Asymptotic inference for partially observed branching processes

Victor Panaretos

We consider the problem of estimation in a partially observed discrete-time Galton-Watson branching process, focusing on the first two moments of the offspring distribution. Our study is motivated by modelling the counts of new cases at the onset of a stoc ...

Applied Probability Trust2011

Stratégies de compromis pour l'estimation des paramètres de régression et pour la classification floue

Nicolas Fournier

Model specification is an integral part of any statistical inference problem. Several model selection techniques have been developed in order to determine which model is the best one among a list of possible candidates. Another way to deal with this questi ...

EPFL2011

Maximum likelihood estimation for multiple-source losss tomography with network coding

Christina Fragouli

Loss tomography aims at inferring the loss rate of links in a network from end-to-end measurements. Previous work in [1] has developed optimal maximum likelihood estimators (MLEs) for link loss rates in a single-source multicast tree. However, only sub-opt ...

2011

Anisotropic error control for environmental applications

Luca Dede'

In this paper we aim at controlling physically meaningful quantities with emphasis on environmental applications. This is carried out by an efficient numerical procedure combining the goal-oriented framework [R. Becker, R. Rannacher, An optimal control app ...

Elsevier2008

Calibration from statistical properties of the visual world

What does a blind entity need in order to determine the geometry of the set of photocells that it carries through a changing lightfield? In this paper, we show that very crude knowledge of some statistical properties of the environment is sufficient for th ...

2008