  We consider the problem of recovering the unknown noise variance in the
linear regression model. To estimate the nuisance (a vector of regression
coefficients) we use a family of spectral regularisers of the maximum
likelihood estimator. The noise estimation is based on the adaptive
normalisation of the squared error. We derive the upper bound for the
concentration of the proposed method around the ideal estimator (the case of
zero nuisance).
