Bondy Tail Sensitivity#
import chainladder as cl
This example demonstrates the usage of the TailBondy estimator as well as
passing multiple scoring functions to GridSearch. When the earliest_age
is set to the last available in the Triangle, the estimator reverts to the
traditional Bondy method.
# Fit basic development to a triangle
tri = cl.load_sample('tail_sample')['paid']
dev = cl.Development(average='simple').fit_transform(tri)
# Return both the tail factor and the Bondy exponent in the scoring function
scoring = {
'tail_factor': lambda x: x.tail_.values[0,0],
'bondy_exponent': lambda x : x.b_.values[0,0]}
# Vary the 'earliest_age' assumption in GridSearch
param_grid=dict(earliest_age=list(range(12, 120, 12)))
grid = cl.GridSearch(cl.TailBondy(), param_grid, scoring)
results = grid.fit(dev).results_
Show code cell source
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%config InlineBackend.figure_format = 'retina'
ax = results.plot(x='earliest_age', y='bondy_exponent',
title='Bondy Assumption Sensitivity', marker='o')
results.plot(x='earliest_age', y='tail_factor', grid=True,
secondary_y=True, ax=ax, marker='o');
