chainladder.TailClark

class chainladder.TailClark(growth='loglogistic', truncation_age=None, attachment_age=None, projection_period=12)

Allows for extraploation of LDFs to form a tail factor.

New in version 0.6.4.

Parameters:

growth: {‘loglogistic’, ‘weibull’}

The growth function to be used in curve fitting development patterns. Options are ‘loglogistic’ and ‘weibull’

truncation_age: int

The age at which you wish to stop extrapolating development

attachment_age: int (default=None)

The age at which to attach the fitted curve. If None, then the latest age is used. Measures of variability from original ldf_ are retained when being used in conjunction with the MackChainladder method.

projection_period: int

The number of months beyond the latest available development age the ldf_ and cdf_ vectors should extend.

Attributes:

ldf_:

ldf with tail applied.

cdf_:

cdf with tail applied.

tail_: DataFrame

Point estimate of tail at latest maturity available in the Triangle.

theta_: DataFrame

Estimates of the theta parameter of the growth curve.

omega_: DataFrame

Estimates of the omega parameter of the growth curve.

elr_: DataFrame

The Expected Loss Ratio parameter. This only exists when a sample_weight is provided to the Estimator.

scale_: DataFrame

The scale parameter of the model.

norm_resid_: Triangle

The “Normalized” Residuals of the model according to Clark.

Methods

fit(X[, y, sample_weight])	Fit the model with X.
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])	Request metadata 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)	Transform X.

pipe