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Hartl, Thomas. 2017. “A Comparison of Resampling Methods for Bootstrapping Triangle GLMs.” Variance 11 (1–2): 60–73.
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  • Figure 1. Impact of different values of C

Abstract

Bootstrapping is often employed for quantifying the inherent variability of development triangle GLMs. While easy to implement, bootstrapping approaches frequently break down when dealing with actual data sets. Often this happens because linear rescaling leads to negative values in the resampled incremental development data. We introduce two computationally efficient methods for avoiding this pitfall: split-linear rescaling and parametric resampling using a limited Pareto distribution. After describing the essential mathematical properties of the techniques, we present a performance comparison based on a VBA for Excel bootstrapping application. The VBA application is available on request from the author.