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Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model.It chooses an equilibrium with a sparse regression method by iteratively estiating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level.The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold.