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Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Sharp convergence rates for forward regression in high-dimensional sparse linear models
Organization Unit
  • Damian Kozbur
  • English
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.
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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