Sharp convergence rates for forward regression in high-dimensional sparse linear models

Damian Kozbur

No 253, ECON - Working Papers from Department of Economics - University of Zurich

Abstract: 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.

Keywords: Forward regression; high-dimensional models; sparsity; model selection (search for similar items in EconPapers)
Date: 2017-05, Revised 2018-04
New Economics Papers: this item is included in nep-dcm and nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.zora.uzh.ch/id/eprint/137364/7/econwp253.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zur:econwp:253

Access Statistics for this paper

More papers in ECON - Working Papers from Department of Economics - University of Zurich Contact information at EDIRC.
Bibliographic data for series maintained by Severin Oswald ().