Publications associées à Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

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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

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

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

MATHICSE Technical Report : Reduced order methods for uncertainty quantification problems

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

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

Time–Data Tradeoffs by Aggressive Smoothing

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

Multitask diffusion LMS with sparsity-based regularization