  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.
