chainladder.TailConstant

class chainladder.TailConstant(tail=1.0, decay=0.5, attachment_age=None, projection_period=12)

Allows for the entry of a constant tail factor to LDFs.

Parameters:

tail: float

The constant to apply to all LDFs within a triangle object.

decay: float (default=0.50)

An exponential decay constant that allows for decay over future development periods. A decay rate of 0.5 sets the development portion of each successive LDF to 50% of the previous LDF.

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.

See also

TailCurve

Notes

The tail constant does not support the entry of variability parameters necessary for stochastic approaches, so any usage of TailConstant will be inherently deterministic.

Attributes:

ldf_:

ldf with tail applied.

cdf_:

cdf with tail applied.

tail_: DataFrame

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

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)	If X and self are of different shapes, align self to X, else return self.

pipe