chainladder.BarnettZehnwirth

class chainladder.BarnettZehnwirth(drop=None, drop_valuation=None, formula=None, response=None, alpha=None, gamma=None, iota=None)

This estimator enables modeling from the Probabilistic Trend Family as described by Barnett and Zehnwirth.

Added in version 0.8.2.

Parameters:

drop: tuple or list of tuples

Drops specific origin/development combination(s)

drop_valuation: str or list of str (default = None)

Drops specific valuation periods. str must be date convertible.

formula: formula-like

A patsy formula describing the independent variables, X of the GLM

response: str

Column name for the reponse variable of the GLM. If ommitted, then the first column of the Triangle will be used.

alpha: list of int

List of origin periods denoting the first indices of each group

gamma: list of int

iota: list of int

Attributes:

cdf_

coef_

cum_zeta_

full_expectation_

full_triangle_

has_ldf

has_zeta

ibnr_

ldf_

triangle_ml_

Methods

fit_transform(X[, y])	Fit to data, then transform it.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
set_backend(backend[, inplace, deep])	Converts triangle array_backend.
set_fit_request(*[, sample_weight])	Configure whether metadata should be requested to be passed to the fit method.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
to_json()	Serializes triangle object to json format
to_pickle(path[, protocol])	Serializes triangle object to pickle.
transform(X)	If X and self are of different shapes, align self to X, else return self.

fit
pipe