Estimating LASSO Risk and Noise Level

NeurIPS 2013 Mohsen BayatiMurat A. ErdogduAndrea Montanari

We study the fundamental problems of variance and risk estimation in high dimensional statistical modeling. In particular, we consider the problem of learning a coefficient vector $\theta_0\in R^p$ from noisy linear observation $y=X\theta_0+w\in R^n$ and the popular estimation procedure of solving an $\ell_1$-penalized least squares objective known as the LASSO or Basis Pursuit DeNoising (BPDN)... (read more)

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