Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/173413 
Authors: 
Year of Publication: 
2017
Series/Report no.: 
Working Paper No. 253
Publisher: 
University of Zurich, Department of Economics, 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 ß-min or irrepresentability conditions.
Subjects: 
Forward regression
high-dimensional models
sparsity
model selection
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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