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Robust compressive sensing of sparse signals: A review

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Robust Multivariate and Nonlinear Time Series Models

Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables lik ...

EPFL2010

Robust inference for generalized linear models

Sahar Hosseinian Ehrensberger

Generalized Linear Models have become a commonly used tool of data analysis. Such models are used to fit regressions for univariate responses with normal, gamma, binomial or Poisson distribution. Maximum likelihood is generally applied as fitting method. I ...

EPFL2009

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

Compressed sensing (CS) deals with the reconstruction of sparse signals from a small number of linear measurements. One of the main challenges in CS is to find the support of a sparse signal from a set of noisy observations. In the CS literature, several i ...

2009

Support Recovery in Compressed Sensing: An Estimation Theoretic Approach

Martin Vetterli, Amin Karbasi, Ali Hormati

2009

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development ...

EPFL2008

Redescending M-estimators

Stephan Morgenthaler

in finite sample studies redescending M-estimators outperform bounded M-estimators (see for example, Andrews et al. [1972. Robust Estimates of Location. Princeton University Press, Princeton]). Even though redescenders arise naturally out of the maximum li ...

Elsevier2008

Differences between observation and sampling error in sparse signal reconstruction

Michael Christoph Gastpar

The field of Compressed Sensing has shown that a relatively small number of random projections provide sufficient information to accurately reconstruct sparse signals. Inspired by applications in sensor networks in which each sensor is likely to observe a ...

Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa2007

Optimal Conditionally Unbiased Bounded-Influence Inference in Dynamic Location and Scale Models

Loriano Mancini, Fabio Trojani

This paper studies the local robustness of estimators and tests for the conditional location and scale parameters in a strictly stationary time series model. We first derive optimal bounded-influence estimators for such settings under a conditionally Gauss ...

2005

Assessing toxic impacts on aquatic ecosystems in life cycle assessment (LCA)

Jérôme Payet

In the last decade, several Life Cycle Assessment (LCA) methods for assessing impact of products on living resources have been developed. Beyond the quantified assessments of impacts on living systems, it also checked the feasibility of the impact assessme ...

EPFL2004

Outlier modeling in image matching

Martin Vetterli, Sabine Süsstrunk, Luciano Sbaiz, David Hasler

We address the question of how to characterize the outliers that may appear when matching two views of the same scene. The match is performed by comparing the difference of the two views at a pixel level, aiming at a better registration of the images. When ...

2003