chainladder.MackChainladder

chainladder.MackChainladder#

class chainladder.MackChainladder#

Basic stochastic chainladder method popularized by Thomas Mack

Parameters:

Attributes:

X_:: returns X
ultimate_:: The ultimate losses per the method
ibnr_:: The IBNR per the method
full_expectation_:: The ultimates back-filled to each development period in X replacing the known data
full_triangle_:: The ultimates back-filled to each development period in X retaining the known data
summary_:: summary of the model
full_std_err_:: The full standard error
total_process_risk_:: The total process error
total_parameter_risk_:: The total parameter error
mack_std_err_:: The total prediction error by origin period
total_mack_std_err_:: The total prediction error across all origin periods

Methods

`fit`(X[, y, sample_weight])	Fit the model with X.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`intersection`(a, b)	Given two Triangles with mismatched indices, this method aligns their indices
`predict`(X[, sample_weight])	Predicts the chainladder ultimate on a new triangle X
`set_backend`(backend[, inplace, deep])	Converts triangle array_backend.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, sample_weight])	Request metadata passed to the `predict` method.
`to_json`()	Serializes triangle object to json format
`to_pickle`(path[, protocol])	Serializes triangle object to pickle.