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MATHICSE Technical Report: Eigenfunction martingale estimating functions and filtered data for drift estimation of discretely observed multiscale

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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 rol ...

Ieee2012

Reconstruction of Biomedical Images and Sparse Stochastic Modeling

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

IEEE2012

Speech Enhancement using an Improved MMSE Estimator with Laplacian Prior

In this paper we present an optimal estimator of magnitude spectrum for speech enhancement when the clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are modeled by a Gaussian distribution. Chen has alread ...

2010

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

Distributed top-k aggregation queries at large

Sebastian Michel

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper intr ...

2009

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

An adaptive freeway traffic state estimator

Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretc ...

2009

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