Description Usage Arguments Details Value

This function estimates the noise level *σ* in linear regression
with possible presence of outliers, based on "lasso-refitting" strategy.

1 | ```
estimateSigma(y, X)
``` |

`y, ` |
the response. |

`X, ` |
the design matrix with the first column being 1 (the column of intercepts). |

Assume the mean-shift model

*y = X β + u + ε,*

where
*ε ~ N(0, σ^2 I)*. This is equivalent to

*y = X.enlarged β + ε,*

where *X.enlarged = (X : I_n)*.
This function fits a lasso regression based on *(y, X.enlarged)*
with the cross-validated tuning parameter,
and then computes the residual sum of square, scaled by *1/(n-s)*,
where *s* is the number of active variables estimated by lasso.

This function returns an estimate of the noise level *σ*.

shuxiaoc/outference documentation built on Dec. 5, 2017, 3:48 a.m.

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