Title
Regression To A Linear Lower Bound With Outliers: An Exponentially Modified Gaussian Noise Model
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.
Year
DOI
Venue
2019
10.23919/EUSIPCO.2019.8902946
2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Julien Gori193.16
Olivier Rioul29223.54