Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models

Damian Kozbur

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arXiv · 1702.01000

Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models

Damian Kozbur

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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 beta-min or irrepresentability conditions.

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Damian Kozbur. 2017-02-03. Sharp Convergence Rates for Forward Regression in High-Dimensional Sparse Linear Models. https://arxiv.org/abs/1702.01000

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