DocumentCode :
336896
Title :
An inverse problem approach to robust regression
Author :
Fuchs, Jean-Jacques
Author_Institution :
Rennes I Univ., France
Volume :
4
fYear :
1999
fDate :
15-19 Mar 1999
Firstpage :
1809
Abstract :
When recording data, large errors may occur occasionally. The corresponding abnormal data points, called outliers, can have drastic effects on the estimates. There are several ways to cope with outliers-detect and delete or adjust the erroneous data,-use a modified cost function. We propose a new approach that allows, by introducing additional variables, to model the outliers and to detect their presence. In the standard linear regression model this leads to a linear inverse problem that, associated with a criterion that ensures sparseness, is solved by a quadratic programming algorithm. The new approach (model+criterion) allows for extensions that cannot be handled by the usual robust regression methods
Keywords :
error analysis; inverse problems; maximum likelihood estimation; noise; quadratic programming; signal detection; statistical analysis; abnormal data points; data recording; errors; linear inverse problem; linear regression model; maximum likelihood estimator; modified cost function; noisy data; outliers detection; quadratic programming algorithm; robust estimate; robust regression methods; sparseness; Additive noise; Cost function; Inverse problems; Least squares methods; Linear regression; Maximum likelihood estimation; Noise robustness; Parameter estimation; Quadratic programming; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1999. Proceedings., 1999 IEEE International Conference on
Conference_Location :
Phoenix, AZ
ISSN :
1520-6149
Print_ISBN :
0-7803-5041-3
Type :
conf
DOI :
10.1109/ICASSP.1999.758272
Filename :
758272
Link To Document :
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