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Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models
| Content Provider | Scilit |
|---|---|
| Author | Kozbur, Damian |
| Copyright Year | 2017 |
| Description | Journal: SSRN Electronic Journal Forward regression is a statistical model selection and estimation procedure which inductively selects covariates that add predictive power into a working statistical regression model. Once a model is selected, unknown regression parameters are estimated by least squares. This paper analyzes forward regression in high-dimensional sparse linear models. Probabilistic bounds for prediction error norm and number of selected covariates are proved. The analysis in this paper gives sharp rates and does not require β-min or irrepresentability conditions. |
| Related Links | http://arxiv.org/pdf/1702.01000 https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2973258 |
| ISSN | 10914358 |
| e-ISSN | 15565068 |
| DOI | 10.2139/ssrn.2973258 |
| Journal | SSRN Electronic Journal |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2017-05-19 |
| Access Restriction | Open |
| Subject Keyword | Journal: SSRN Electronic Journal Statistics and Probability Forward Regression |
| Content Type | Text |
| Resource Type | Article |
| Subject | Public Health, Environmental and Occupational Health Psychiatry and Mental Health |