# 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

 Initialize self. Estimate censoring distribution from training data. 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.

Returns

Return type

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.

Returns

ipcw – Inverse probability of censoring weights.

Return type

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.

Returns

prob – Probability of an event.

Return type

array, shape = (n_samples,)