# sksurv.nonparametric.CensoringDistributionEstimator¶

class sksurv.nonparametric.CensoringDistributionEstimator[source]

Kaplan–Meier estimator for the censoring distribution.

__init__()[source]

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__() Initialize self. fit(y) Estimate censoring distribution from training data. predict_ipcw(y) Return inverse probability of censoring weights at given time points. predict_proba(time) Return probability of an event after given time point.
fit(y)[source]

Estimate censoring distribution from training data.

Parameters: y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field. self
predict_ipcw(y)[source]

Return inverse probability of censoring weights at given time points.

$$\omega_i = \delta_i / \hat{G}(y_i)$$

Parameters: y (structured array, shape = (n_samples,)) – A structured array containing the binary event indicator as first field, and time of event or time of censoring as second field. ipcw – Inverse probability of censoring weights. array, shape = (n_samples,)
predict_proba(time)[source]

Return probability of an event after given time point.

$$\hat{S}(t) = P(T > t)$$

Parameters: time (array, shape = (n_samples,)) – Time to estimate probability at. prob – Probability of an event. array, shape = (n_samples,)