chainladder.ClarkLDF

class chainladder.ClarkLDF(growth: str = 'loglogistic', groupby=None)

An Estimator that allows for curve fitting development patterns according to Clark 2003.

The method fits incremental triangle amounts to one of “loglogistic” or “weibull” growth curves. Both of Clark’s methods, LDF and Cape Cod, can be estimated. To invoke the Cape Cod method, include “sample_weight” in when fitting the estimator.

Parameters:

growth: {‘loglogistic’, ‘weibull’}

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

groupby:

An option to group levels of the triangle index together for the purposes of estimating patterns. If omitted, each level of the triangle index will receive its own patterns.

Attributes:

ldf_: Triangle

The estimated loss development patterns.

cdf_: Triangle

The estimated cumulative development patterns.

incremental_fits_: Triangle

The fitted incrementals of the model.

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

G_(age)	Growth function of the estimator.
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])	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.

pipe