Publications related to Worst-case parameter estimation with bounded model uncertainties

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Andreas Pautz, Mathieu Hursin, Daniel Jerôme Siefman

This paper describes the application of data assimilation methods to CASMO-5 simulations of a Proteus research reactor experiment. Its focus is a comparison and evaluation of three prominent data assimilation methods: generalized linear least squares, MOCA ...

2017

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Martin Vetterli, Ali Zoubir, Marta Martinez-Camara

Solving a linear inverse problem may include difficulties such as the presence of outliers and a mixing matrix with a large condition number. In such cases a regularized robust estimator is needed. We propose a new tau-type regularized robust estimator tha ...

2015

Forecasting Uncertainty in Electricity Demand

Tri Kurniawan Wijaya

Generalized Additive Models (GAM) are a widely popular class of regression models to forecast electricity demand, due to their high accuracy, flexibility and interpretability. However, the residuals of the fitted GAM are typically heteroscedastic and lepto ...

2015

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Afsaneh Asaei, Mohammadjavad Taghizadeh, Saeid Haghighatshoar

In this paper, we prove a new identity for the least-square solution of an over-determined set of linear equation

Ax=b

, where

A

is an

m\times n

full-rank matrix,

b

is a column-vector of dimension

m

, and

m

(the number of equations) is larger tha ...

2015

Robust distributed detection over adaptive diffusion networks

Ali H. Sayed, Ali Zoubir

Diffusion adaptation techniques based on the least-mean-squares criterion have been proposed for distributed detection of a signal in Gaussian-distributed noise, forgoing the need for a fusion center. However, least-mean-squares solutions are generally non ...

IEEE2014

When crowding of crowding leads to uncrowding

Michael Herzog, Bilge Sayim, Mauro Manassi

In object recognition, features are thought to be processed in a hierarchical fashion from low-level analysis (edges and lines) to complex figural processing (shapes and objects). Here, we show that figural processing determines low-level processing. Verni ...

Association for Research in Vision and Ophthalmology2013

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

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

Iterative Estimation of Rigid-Body Transformations Application to Robust Object Tracking and Iterative Closest Point

Aude Billard, Micha Hersch

Closed-form solutions are traditionally used in computer vision for estimating rigid body transformations. Here we suggest an iterative solution for estimating rigid body transformations and prove its global convergence. We show that for a number of applic ...

Springer Verlag2012

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Michaël Unser, Emrah Bostan, Ulugbek Kamilov

We introduce a new wavelet-based method for the implementation of Total-Variation-type denoising. The data term is least-squares, while the regularization term is gradient-based. The particularity of our method is to exploit a link between the discrete gra ...

IEEE2012

Worst-case parameter estimation with bounded model uncertainties

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Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Forecasting Uncertainty in Electricity Demand

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Robust distributed detection over adaptive diffusion networks

When crowding of crowding leads to uncrowding

Robust parameter estimation for the Ornstein-Uhlenbeck process

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

Iterative Estimation of Rigid-Body Transformations Application to Robust Object Tracking and Iterative Closest Point

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Robust parameter estimation for the Ornstein-Uhlenbeck process

Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

When crowding of crowding leads to uncrowding

Wavelet Shrinkage With Consistent Cycle Spinning Generalizes Total Variation Denoising

Robust Parameter Estimation for the Ornstein-Uhlenbeck Process

A new robust and efficient estimator for ill-conditioned linear inverse problems with outliers

Forecasting Uncertainty in Electricity Demand

Robust distributed detection over adaptive diffusion networks

A New Identity for the Least-square Solution of Overdetermined Set of Linear Equations

Iterative Estimation of Rigid-Body Transformations Application to Robust Object Tracking and Iterative Closest Point