BootstrapODPSample Variability

import chainladder as cl

This example demonstrates how you can drop the outlier link ratios from the BootstrapODPSample. This has a direct consquence of reducing reserve variability estimates.

# Load triangle
triangle = cl.load_sample("genins")

# 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] * 5 + [False] * 4,
    drop_low=[True] * 5 + [False] * 4,
    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")

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import matplotlib.pyplot as plt

plt.style.use("ggplot")
%config InlineBackend.figure_format = 'retina'
# Plot both IBNR distributions
ax = results.to_frame().plot(
    kind="hist",
    bins=50,
    alpha=0.5,
    grid=True,
    xlabel="Ultimate",
    title="Reserve Variability",
)