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| Type | Working Paper |
| Scope | Discipline-based scholarship |
| Title | Sharp convergence rates for forward regression in high-dimensional sparse linear models |
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| Institution | University of Zurich |
| Series Name | Working paper series / Department of Economics |
| Number | 253 |
| ISSN | 1664-7041 |
| Number of Pages | 18 |
| Date | 2018 |
| Abstract Text | 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. |
| Official URL | http://www.econ.uzh.ch/static/wp/econwp253.pdf |
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| Keywords | Forward regression, high-dimensional models, sparsity, model selection, Regressionsanalyse, Modellwahl, Lineares Modell, Prognoseverfahren |
| Additional Information | Revised version Auch erschienen in: arXiv: 1702.01000v3 |