statsmodels.duration.hazard_regression.PHReg.fit_regularized¶

PHReg.
fit_regularized
(method='elastic_net', alpha=0.0, start_params=None, refit=False, **kwargs)[source]¶ Return a regularized fit to a linear regression model.
Parameters: method :
Only the elastic_net approach is currently implemented.
alpha : scalar or arraylike
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.
start_params : arraylike
Starting values for params.
refit : bool
If True, the model is refit using only the variables that have nonzero coefficients in the regularized fit. The refitted model is not regularized.
Returns: A results object.
Notes
The penalty is the
elastic net
penalty, which is a combination of L1 and L2 penalties.The function that is minimized is: ..math:
loglike/n + alpha*((1L1_wt)*params_2^2/2 + L1_wt*params_1)
where and are the L1 and L2 norms.
Postestimation results are based on the same data used to select variables, hence may be subject to overfitting biases.
The elastic_net method uses the following keyword arguments:
 maxiter : int
 Maximum number of iterations
 L1_wt : float
 Must be in [0, 1]. The L1 penalty has weight L1_wt and the L2 penalty has weight 1  L1_wt.
 cnvrg_tol : float
 Convergence threshold for line searches
 zero_tol : float
 Coefficients below this threshold are treated as zero.