Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2013 | Control Conference | maximum likelihood estimation,√n-consistent estimator,m-estimator,oracle property,asymptotic variable selection,automatic tuning,general model structures,implicit regularization parameter,maximum likelihood method,prediction error method,sparse estimation technique,sparseness pattern,statistical analysis,tuning,cost function,m estimator,vectors,control engineering |
Field | DocType | Citations |
Efficient estimator,Minimum-variance unbiased estimator,Mathematical optimization,Stein's unbiased risk estimate,Algorithm,Bias of an estimator,Estimation theory,Bayes estimator,Mathematics,Estimator,Consistent estimator | Conference | 2 |
PageRank | References | Authors |
0.39 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristian R. Rojas | 1 | 252 | 43.97 |
| B. Wahlberg | 2 | 180 | 25.61 |
| Håkan Hjalmarsson | 3 | 1254 | 175.16 |