Publications associées à Biais (statistique)

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...

2020

Context effects on probability estimation

Wei-Hsiang Lin

Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probabili ...

PUBLIC LIBRARY SCIENCE2020

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

Ali H. Sayed, Roula Nassif

In this article, we are interested in adaptive and distributed estimation of graph filters from streaming data. We formulate this problem as a consensus estimation problem over graphs, which can be addressed with diffusion LMS strategies. Most popular grap ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients

Andreas Pautz, Mathieu Hursin, Daniel Jerôme Siefman

Sensitivity coefficients from Monte Carlo neutron transport codes have uncertainties that can affect nuclear data adjustments with integral experiments. This paper presents an extended version of Generalized Linear Least Squares (GLLS), called xGLLS, that ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Comment: Models Are Approximations!

Anthony Christopher Davison, Erwan Fabrice Koch, Jonathan Koh Boon Han

This discussion focuses on areas of disagreement with the papers, particularly the target of inference and the case for using the robust 'sandwich' variance estimator in the presence of moderate mis-specification. We also suggest that existing procedures m ...

INST MATHEMATICAL STATISTICS2019

Sequential model identification of reaction systems-The missing path between the incremental and simultaneous approaches

Dominique Bonvin, Julien Léo Billeter, Sriniketh Srinivasan

Modeling chemical reaction systems is an important but complex task. The identified kinetic model must be able to explain all the underlying rate processes such as chemical reactions and heat and mass transfers. Traditionally, the modeling task is carried ...

2019

Central limit theorems for multilevel Monte Carlo methods

Sebastian Krumscheid, Hakon Andreas Hoel

In this work, we show that uniform integrability is not a necessary condition for central limit theorems (CLT) to hold for normalized multilevel Monte Carlo (MLMC) estimators and we provide near optimal weaker conditions under which the CLT is achieved. In ...

2019

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

Victor Panaretos, Anirvan Chakraborty

We consider the problem of estimating the slope function in a functional regression with a scalar response and a functional covariate. This central problem of functional data analysis is well known to be ill-posed, thus requiring a regularised estimation p ...

2019

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Biais (statistique)

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Context effects on probability estimation

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

A causal framework for classical statistical estimands in failure-time settings with competing events

On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients

Adversarial Analytics

Comment: Models Are Approximations!

Sequential model identification of reaction systems-The missing path between the incremental and simultaneous approaches

Central limit theorems for multilevel Monte Carlo methods

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

Graph Chatbot

Chattez avec Graph Search

Central limit theorems for multilevel Monte Carlo methods

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

Context effects on probability estimation

Online Distributed Learning Over Graphs With Multitask Graph-Filter Models

A causal framework for classical statistical estimands in failure-time settings with competing events

Adversarial Analytics

On data assimilation with Monte-Carlo-calculated and statistically uncertain sensitivity coefficients

Hybrid regularisation and the (in)admissibility of ridge regression in infinite dimensional Hilbert spaces

Comment: Models Are Approximations!

Sequential model identification of reaction systems-The missing path between the incremental and simultaneous approaches