## On the “degrees of freedom” of the lasso.(English)Zbl 1126.62061

Summary: We study the effective degrees of freedom of the lasso in the framework of C. M. Stein’s [Ann. Stat. 9, 1135–1151 (1981; Zbl 0476.62035)] unbiased risk estimation (SURE). We show that the number of nonzero coefficients is an unbiased estimate for the degrees of freedom of the lasso – a conclusion that requires no special assumptions on the predictors. In addition, the unbiased estimator is shown to be asymptotically consistent. With these results on hand, various model selection criteria, $$C_p$$, AIC and BIC, are available, which, along with the least angle regression (LARS) algorithm, provide a principled and efficient approach to obtaining the optimal lasso fit with the computational effort of a single ordinary least-squares fit.

### MSC:

 62J05 Linear regression; mixed models 90C46 Optimality conditions and duality in mathematical programming 65C60 Computational problems in statistics (MSC2010) 62J07 Ridge regression; shrinkage estimators (Lasso)

### Keywords:

LARS algorithm; lasso; model selection; SURE; unbiased estimate

Zbl 0476.62035

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### References:

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