Regression to a linear lower bound with outliers: An exponentially modified Gaussian noise model

Julien Gori, Olivier Rioul

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A regression method to estimate a linear bound in the presence of outliers is discussed. An exponentially-modified Gaussian (EMG) noise model is proposed, based on a maximum entropy argument. The resulting “EMG regression” method is shown to encompass the classical linear regression (with Gaussian noise) and a minimum regression (with exponential noise) as special cases. Simulations are performed to assess the consistency of the regression as well as its resilience to model mismatch. We conclude with an example taken from a real-world study of human performance in rapid aiming with application to human-computer interaction.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - 1 Sept 2019
Externally publishedYes
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sept 20196 Sept 2019

Publication series

NameEuropean Signal Processing Conference
Volume2019-September
ISSN (Print)2219-5491

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
Country/TerritorySpain
CityA Coruna
Period2/09/196/09/19

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