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Bivariate Extreme Statistics, Ii

Publications associées (42)

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Wavelet-variance-based estimation for composite stochastic processes

Jan Skaloud, Stéphane Guerrier, Yannick Stebler

This article presents a new estimation method for the parameters of a time series model. We consider here composite Gaussian processes that are the sum of independent Gaussian processes which, in turn, explain an important aspect of the time series, as is ...

Amer Statistical Assoc2013

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

Recent Progress in Polymorphism-Based Population Genetic Inference

Jeffrey David Jensen

The recent availability of whole-genome sequencing data affords tremendous power for statistical inference. With this, there has been great interest in the development of polymorphism-based approaches for the estimation of population genetic parameters. Th ...

Oxford University Press2012

Statistical Modeling of Spatial Extremes

Anthony Christopher Davison, Simone Padoan

The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the ...

Institute of Mathematical Statistics2012

STATISTICAL MODELING OF THE EYE FOR MULTIMODAL TREATMENT PLANNING FOR EXTERNAL BEAM RADIOTHERAPY OF INTRAOCULAR TUMORS

Meritxell Bach Cuadra, Alessia Pica

A statistical shape model is a suitable tool for high accuracy eye modeling required for treatment planning for external beam radiotherapy of intraocular tumors. In this study, the automatically adapted eye model has been compared to manual segmentation ba ...

Elsevier Science Inc2012

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

Population Size Estimation Using a Few Individuals as Agents

Martin Vetterli, Patrick Thiran, Olivier Dousse, Farid Movahedi Naini

We conduct an experiment where ten attendees of an open-air music festival are acting as Bluetooth probes. We then construct a parametric statistical model to estimate the total number of visible Bluetooth devices in the festival area. By comparing our est ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2011

Statistical modeling of inter-device correlations with BPV

Ivica Stevanovic, Xuan Li

The backward propagation of variance (BPV) technique for statistical modeling has proven to be efficient and effective in practice. In this paper we extend the BPV formalism to explicitly include modeling of the correlations between electrical performances ...

Elsevier2010

Distributed bearing estimation via matrix completion

Volkan Cevher

We consider bearing estimation of multiple narrow-band plane waves impinging on an array of sensors. For this problem, bearing estimation algorithms such as minimum variance distortionless response (MVDR), multiple signal classification, and maximum likeli ...

2010

Likelihood-Based Inference for Max-Stable Processes

Simone Padoan

The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes However, their application is complicated due to the unavailability of the multivariate density function and so likehhood-based methods r ...

American Statistical Association2010