Publication details
Air pollution analysis based on sparse estimates from an overcomplete model
| Authors | |
|---|---|
| Year of publication | 2006 |
| Type | Article in Proceedings |
| Conference | Program and Abstracts, The Seventeenth International Conference on Qualitative Methods for the Environmental Sciences, TIES 2006 |
| MU Faculty or unit | |
| Citation | |
| Field | Applied statistics, operation research |
| Keywords | Air Pollution; Sparse estimates; Overcomplete model; |
| Description | For the analysis of air pollution by suspended particulate matter (PM_10) in the city of Brno (Czech Republic) an overcomplete ARX model involving polynomial trend component has been used. We apply a new sparse parameter estimation technique based on the Basis Pursuit Algorithm originally suggested by Chen et al [SIAM Review 43 (2001), No. 1] for time-scale analysis of digital signals and utilizing numerical procedures by the first author and M.A.Saunders. The new approach allows one to reliably identify significantly non-zero parameters in the overparametrized model and thus fix model components relevant to the air pollution mechanism. |
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