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Statistical Inference for Partially Observed Stochastic Epidemics

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Statistical Inference in Positron Emission Tomography

Matthieu Martin Jean-André Simeoni

In this report, we investigate mathematical algorithms for image reconstruction in the context of positron emission tomography (a medical diagnosis technique). We first take inspiration from the physics of PET to design a mathematical model tailored to the ...

2014

Multidimensional Indicator Analysis for Transport Policy Evaluation

Michel Bierlaire, Bilal Farooq, Dimitrios Efthymiou

The need for forecasting the direct and indirect effects of land use and transport policies on society, the environment, and the local economy has led to the development of integrated land use and transport (LUTI) models. The land use and transport policy ...

Natl Acad Sciences2014

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

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

MAP Estimators for Self-Similar Sparse Stochastic Models

Michaël Unser, Julien René Pierre Fageot, Emrah Bostan, Ulugbek Kamilov

We consider the reconstruction of multi-dimensional signals from noisy samples. The problem is formulated within the framework of the theory of continuous-domain sparse stochastic processes. In particular, we study the fractional Laplacian as the whitening ...

SampTA2013

Bayesian Semiparametrics for Modelling the Clustering of Extreme Values

Thomas Lugrin

Extreme events can be statistically characterised as excesses of a high threshold. Inference in this case has to account for dependence between excesses. The peaks over threshold approach suggests pre-processing the series by defining clusters of successiv ...

2013

Robust parameter estimation for the Ornstein-Uhlenbeck process

Sonja Rieder

In this paper, we derive elementary M- and optimally robust asymptotic linear (AL)-estimates for the parameters of an Ornstein-Uhlenbeck process. Simulation and estimation of the process are already well-studied, see Iacus (Simulation and inference for sto ...

Springer-Verlag2012

La détection de périodicités cachées

Jean-Marc Nicoletti

This thesis is a small part of the preparation of the launch of the Gaia mission, a satellite of the European Space Agency (ESA). One of the goals of the mission is to perform a classification among variable stars considering different attributes. Periodic ...

EPFL2012

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

Sonja Rieder

In this thesis, we treat robust estimation for the parameters of the Ornstein–Uhlenbeck process, which are the mean, the variance, and the friction. We start by considering classical maximum likelihood estimation. For the simulation study, where we also in ...

EPFL2012

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