A Dirichlet-Gamma bootstrap for macro-level claims reserving satisfies the conditioning principle exactly, delivering O(I^{-1/2}) coverage error and inheriting calibration from any development-proportion method.
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A negative binomial chain-ladder model is obtained from a Poisson-Gamma mixture that supplies a generative interpretation for the dispersion parameter and unifies the chain-ladder family under one likelihood.
citing papers explorer
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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, delivering O(I^{-1/2}) coverage error and inheriting calibration from any development-proportion method.
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The Negative Binomial Chain-Ladder: A Full Likelihood Model for Claim Count Reserving
A negative binomial chain-ladder model is obtained from a Poisson-Gamma mixture that supplies a generative interpretation for the dispersion parameter and unifies the chain-ladder family under one likelihood.