Publications related to Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease stati ...

Ieee-Inst Electrical Electronics Engineers Inc2015

Statistical Optimality of Hermite Splines

Michaël Unser, Julien René Pierre Fageot, Virginie Sophie Uhlmann, Harshit Gupta

Hermite splines are commonly used for interpolating data when samples of the derivative are available, in a scheme called Hermite interpolation. Assuming a suitable statistical model, we demonstrate that this method is actually optimal for reconstructing r ...

IEEE2015

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Victor Panaretos, Mikael Johan Kuusela

We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a particle detector. This important statistical inverse problem arising in da ...

Inst Mathematical Statistics2015

Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs

Andrea Manzoni, Federico Negri

In this paper we present some heuristic strategies to compute rapid and reliable approximations to stability factors in nonlinear, inf-sup stable parametrized PDEs. The efficient evaluation of these quantities is crucial for the rapid construction of a pos ...

Springer Verlag2015

Impact of synchrophasor measurement types and uncertainties on the accuracy of distribution system linear state estimators

Mario Paolone, Lorenzo Zanni, Daniele Colangelo

The paper aims at assessing the effects of combined voltage and/or current synchrophasor measurements, and their associated uncertainties, on the accuracy of state estimators adopted in distribution systems. Such an assessment is first carried out with res ...

IEEE2015

MATHICSE Technical Report : Reduced order methods for uncertainty quantification problems

Alfio Quarteroni, Gianluigi Rozza, Peng Chen

This work provides a review on reduced order methods in solving uncertainty quantification problems. A quick introduction of the reduced order methods, including proper orthogonal decomposition and greedy reduced basis methods, are presented along with the ...

MATHICSE2015

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Volkan Cevher, Jonathan Mark Scarlett, Yen-Huan Li

We consider the model selection consistency or sparsistency of a broad set of

\ell_1

-regularized

M

-estimators for linear and non-linear statistical models in a unified fashion. For this purpose, we propose the local structured smoothness condition (LSS ...

2015

Plug-and-play state estimation and application to distributed output-feedback model predictive control

Giancarlo Ferrari Trecate, Marcello Farina

In this paper we propose a novel distributed state estimator for large-scale linear systems composed by subsystems interacting through state variables. The distributed state estimator has the following features: (i) local state estimators, each dedicated t ...

2015

Multitask diffusion LMS with sparsity-based regularization

Ali H. Sayed, Roula Nassif

In this work, a diffusion-type algorithm is proposed to solve multitask estimation problems where each cluster of nodes is interested in estimating its own optimum parameter vector in a distributed manner. The approach relies on minimizing a global mean-sq ...

IEEE2015

Time–Data Tradeoffs by Aggressive Smoothing

Volkan Cevher

This paper proposes a tradeoff between sample complexity and computation time that applies to statistical estimators based on convex optimization. As the amount of data increases, we can smooth optimization problems more and more aggressively to achieve ac ...

2014

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

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Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Statistical Optimality of Hermite Splines

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs

Impact of synchrophasor measurement types and uncertainties on the accuracy of distribution system linear state estimators

MATHICSE Technical Report : Reduced order methods for uncertainty quantification problems

Sparsistency of $\ell_1$-Regularized $M$-Estimators

Plug-and-play state estimation and application to distributed output-feedback model predictive control

Multitask diffusion LMS with sparsity-based regularization

Time–Data Tradeoffs by Aggressive Smoothing

Statistical Unfolding Of Elementary Particle Spectra: Empirical Bayes Estimation And Bias-Corrected Uncertainty Quantification

Statistical Optimality of Hermite Splines

Plug-and-play state estimation and application to distributed output-feedback model predictive control

Multitask diffusion LMS with sparsity-based regularization

Time–Data Tradeoffs by Aggressive Smoothing

Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

Heuristic strategies for the approximation of stability factors in quadratically nonlinear parametrized PDEs

Impact of synchrophasor measurement types and uncertainties on the accuracy of distribution system linear state estimators

MATHICSE Technical Report : Reduced order methods for uncertainty quantification problems

Sparsistency of $\ell_1$-Regularized $M$-Estimators