A Dirichlet-Gamma bootstrap for macro-level claims reserving satisfies the conditioning principle exactly, yielding O(I^{-1/2}) coverage deficit while remaining model-agnostic to Chain-Ladder, Bornhuetter-Ferguson or Cape Cod.
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The paper derives negative binomial incremental claim counts from a Poisson-Gamma mixture, embedding the Chain-Ladder method in a full likelihood framework where the dispersion parameter represents accident-year heterogeneity.
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A Model-Agnostic Bootstrap for Macro-Level Claims Reserving Under the Conditioning Principle
A Dirichlet-Gamma bootstrap for macro-level claims reserving satisfies the conditioning principle exactly, yielding O(I^{-1/2}) coverage deficit while remaining model-agnostic to Chain-Ladder, Bornhuetter-Ferguson or Cape Cod.
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The Negative Binomial Chain-Ladder: A Full Likelihood Model for Claim Count Reserving
The paper derives negative binomial incremental claim counts from a Poisson-Gamma mixture, embedding the Chain-Ladder method in a full likelihood framework where the dispersion parameter represents accident-year heterogeneity.