hidimstat.reid

hidimstat.reid(X, y, eps=0.01, tol=0.0001, max_iter=10000, n_jobs=1, seed=0)

Estimation of noise standard deviation using Reid procedure

Parameters:

Xndarray, shape (n_samples, n_features)

Data.

yndarray, shape (n_samples,)

Target.

eps: float, optional (default=1e-2)

Length of the cross-validation path. eps=1e-2 means that alpha_min / alpha_max = 1e-2.

tolfloat, optional (default=1e-4)

The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

max_iterint, optional (default=1e4)

The maximum number of iterations.

n_jobsint or None, optional (default=1)

Number of CPUs to use during the cross validation.

seed: int, optional (default=0)

Seed passed in the KFold object which is used to cross-validate LassoCV. This seed controls the partitioning randomness.

Returns:

sigma_hatfloat

Estimated noise standard deviation.

beta_hatarray, shape (n_features,)

Estimated parameter vector.

References

[1]

Reid, S., Tibshirani, R., & Friedman, J. (2016). A study of error variance estimation in lasso regression. Statistica Sinica, 35-67.