DocumentCode
17207
Title
A Regularized Estimator For Linear Regression Model With Possibly Singular Covariance
Author
Hoang, Hong Son ; Baraille, Rémy
Author_Institution
SHOM/HOM, Toulouse, France
Volume
58
Issue
1
fYear
2013
fDate
Jan. 2013
Firstpage
236
Lastpage
241
Abstract
A regularized estimator is proposed for regression models in the case where the covariances may be singular. Conditions guaranteeing proximity of a regularized estimator to the optimal estimator are obtained by appropriate choice of regularization parameters by allowing a prescribed level of uncertainty. A simple Monte-Carlo simulation study is reported to highlight some aspects and performance of the proposed approach.
Keywords
Monte Carlo methods; covariance matrices; regression analysis; Monte-Carlo simulation; linear regression model; optimal estimator; regularized estimator; singular covariance; Convergence; Eigenvalues and eigenfunctions; Estimation error; Mathematical model; Stability analysis; Vectors; Covariance matrix; LMS algorithm; linear regression system; parameter estimation; regularization;
fLanguage
English
Journal_Title
Automatic Control, IEEE Transactions on
Publisher
ieee
ISSN
0018-9286
Type
jour
DOI
10.1109/TAC.2012.2203552
Filename
6213506
Link To Document