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=1e07, zero_tol=1e08, **kwargs)[source]¶ 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 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.
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 : arraylike
Starting values for params.
cnvrg_tol : scalar
If params changes by less than this amount (in supnorm) 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*((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.