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%matplotlib inline

ODP Bootstrap Comparison

This example demonstrates how you can drop the outlier link ratios from the BootstrapODPSample to reduce reserve variability estimates.



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import chainladder as cl

import seaborn as sns
sns.set_style('whitegrid')

# Load triangle
triangle = cl.load_dataset('raa')

# Use bootstrap sampler to get resampled triangles
s1 = cl.BootstrapODPSample(
    n_sims=5000, random_state=42).fit(triangle).resampled_triangles_

## Alternatively use fit_transform() to access resampled triangles dropping
#  outlier link-ratios from resampler
s2 = cl.BootstrapODPSample(
    drop_high=True, drop_low=True,
    n_sims=5000, random_state=42).fit_transform(triangle)

# Summarize results of first model
results = cl.Chainladder().fit(s1).ibnr_.sum('origin').rename('columns', ['Original'])
# Add another column to triangle with second set of results.
results['Dropped'] = cl.Chainladder().fit(s2).ibnr_.sum('origin')

# Plot both IBNR distributions
results.to_frame().plot(kind='hist', bins=50, alpha=0.5)