statsmodels.duration.hazard_regression.PHReg.fit_regularized

PHReg.fit_regularized(method='coord_descent', maxiter=100, alpha=0.0, L1_wt=1.0, start_params=None, cnvrg_tol=1e-07, zero_tol=1e-08, **kwargs)

Return a regularized fit to a linear regression model.

Parameters:

method :

Only the coordinate descent algorithm is implemented.

maxiter : integer

The maximum number of iteration cycles (an iteration cycle involves running coordinate descent on all variables).

alpha : scalar or array-like

The penalty weight. If a scalar, the same penalty weight applies to all variables in the model. If a vector, it must have the same length as params, and contains a penalty weight for each coefficient.

L1_wt : scalar

The fraction of the penalty given to the L1 penalty term. Must be between 0 and 1 (inclusive). If 0, the fit is a ridge fit, if 1 it is a lasso fit.

start_params : array-like

Starting values for params.

cnvrg_tol : scalar

If params changes by less than this amount (in sup-norm) in once iteration cycle, the algorithm terminates with convergence.

zero_tol : scalar

Any estimated coefficient smaller than this value is replaced with zero.

Returns:

A PHregResults object, of the same type returned by fit.

Notes

The penalty is the”elastic net” penalty, which is a convex combination of L1 and L2 penalties.

The function that is minimized is: ..math:

-loglike/n + alpha*((1-L1_wt)*|params|_2^2/2 + L1_wt*|params|_1)

where and are the L1 and L2 norms.

Post-estimation results are based on the same data used to select variables, hence may be subject to overfitting biases.