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%matplotlib inline

ODP Bootstrap Example

This example demonstrates how you can can use the Overdispersed Poisson Bootstrap sampler and get various properties about parameter uncertainty.



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import chainladder as cl

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')


#  Grab a Triangle
tri = cl.load_dataset('genins')
# Generate bootstrap samples
sims = cl.BootstrapODPSample().fit_transform(tri)
# Calculate LDF for each simulation
sim_ldf = cl.Development().fit(sims).ldf_
sim_ldf = sim_ldf[sim_ldf.origin==sim_ldf.origin.max()]

# Plot the Data
fig, ((ax00, ax01), (ax10, ax11)) = plt.subplots(ncols=2, nrows=2, figsize=(10,10))
tri.T.plot(ax=ax00).set(title='Raw Data', xlabel='Development', ylabel='Incurred')
sims.mean().T.plot(ax=ax01).set(title='Mean Simulation', xlabel='Development', ylabel='Incurred')
sim_ldf.T.plot(legend=False, color='lightgray', ax=ax10) \
       .set(title='Simulated LDF', xlabel='Development', ylabel='LDF')
cl.Development().fit(tri).ldf_.drop_duplicates().T \
                .plot(legend=False, color='red', ax=ax10)
sim_ldf.T.loc['12-24'].plot(kind='hist', bins=50, alpha=0.5, ax=ax11) \
                      .set(title='Age 12-24 LDF Distribution', xlabel='LDF');