A sparse estimation technique for general model structures
2013 (English)In: 2013 European Control Conference, ECC 2013, IEEE , 2013, 2410-2414 p.Conference paper (Refereed)
In this paper, a general sparse estimator is proposed, based on the maximum likelihood / prediction error method (or any √N-consistent estimator). This procedure does not rely on the convexity of the cost function of the underlying estimator (in case such estimator is an M-estimator), and it provides an automatic tuning of the (implicit) regularization parameter. The idea behind the proposed method is a three step procedure, where the first step consists in a standard √N-consistent estimation, the second step seeks for the sparsest estimate in a neighborhood of the initial estimate, and the last step is a refinement based on the sparseness pattern estimated in the second step. A rigorous statistical analysis is provided, which establishes conditions for consistency, asymptotic variable selection and the so-called Oracle property. A simulation example is given to demonstrate the performance of the method.
Place, publisher, year, edition, pages
IEEE , 2013. 2410-2414 p.
Automatic tuning, Initial estimate, Oracle properties, Prediction error method, Regularization parameters, Simulation example, Sparse estimation, Variable selection
IdentifiersURN: urn:nbn:se:kth:diva-137316ISI: 000332509702133ScopusID: 2-s2.0-84893327021ISBN: 978-303303962-9OAI: oai:DiVA.org:kth-137316DiVA: diva2:678710
2013 12th European Control Conference, ECC 2013; Zurich; Switzerland; 17 July 2013 through 19 July 2013
QC 201403192013-12-122013-12-122014-04-24Bibliographically approved