chainladder.MunichAdjustment

class chainladder.MunichAdjustment(paid_to_incurred=None, fillna=False)

Applies the Munich Chainladder adjustment to a set of paid/incurred

ldfs. The Munich method heavily relies on the ratio of paid/incurred and its inverse.

Parameters:

paid_to_incurred: tuple or list of tuples

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

fillna: boolean

The MunichAdjustment will fail when P/I or I/P ratios cannot be calculated. Setting fillna to True will fill the triangle with expected amounts using the simple chainladder.

Attributes:

basic_cdf_: Triangle

The univariate cumulative development patterns

basic_sigma_: Triangle

Sigma of the univariate ldf regression

resids_: Triangle

Residuals of the univariate ldf regression

q_: Triangle

chainladder age-to-age factors of the paid/incurred triangle and its inverse. For paid measures it is (P/I) and for incurred measures it is (I/P).

q_resids_: Triangle

Residuals of q regression.

rho_: Triangle

Estimated conditional deviation around q_

lambda_: Series or DataFrame

Dependency coefficient between univariate chainladder link ratios and q_resids_

ldf_: Triangle

The estimated bivariate loss development patterns

cdf_: Triangle

The estimated bivariate cumulative development patterns

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