chainladder.CaseOutstanding

class chainladder.CaseOutstanding(paid_to_incurred=None, paid_n_periods=-1, case_n_periods=-1, groupby=None)

A determinisic method based on outstanding case reserves.

The CaseOutstanding method is a deterministic approach that develops patterns of incremental payments as a percent of previous period case reserves as well as patterns for case reserves as a percent of previous period case reserves. Although the patterns produces by the approach approximate incremental payments and case outstanding, they are converted into comparable multiplicative patterns for usage with the various IBNR methods.

New in version 0.8.0.

Parameters:

paid_to_incurred: tuple or list of tuples

A tuple representing the paid and incurred columns of the triangles such as ('paid', 'incurred')

paid_n_periods: integer, optional (default=-1)

number of origin periods to be used in the paid pattern averages. For all origin periods, set paid_n_periods=-1

case_n_periods: integer, optional (default=-1)

number of origin periods to be used in the case pattern averages. For all origin periods, set paid_n_periods=-1

Attributes:

ldf_: Triangle

The estimated (multiplicative) loss development patterns.

cdf_: Triangle

The estimated (multiplicative) cumulative development patterns.

case_to_prior_case_: Triangle

The case to prior case ratios used for fitting the estimator

case_ldf_:

The selected case to prior case ratios of the fitted estimator

paid_to_prior_case_: Triangle

The paid to prior case ratios used for fitting the estimator

paid_ldf_:

The selected paid to prior case ratios of the fitted estimator

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