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<p>Likelihood-based inference for exponential-tail lifetime models can be unstable in finite samples, particularly under right-tail contamination. We develop a robust score-based inference procedure for the two-parameter Gompertz distribution by constructing estimating equations in which influence is bounded after standardisation by the local curvature of the likelihood, implemented via OPG-whitened score contributions and Huber weighting. Uncertainty is quantified using a Godambe (sandwich) covariance estimator, enabling Wald-type inference. Simulation results under clean sampling and controlled right-tail contamination show improved stability and more reliable coverage than maximum likelihood, with a moderate efficiency loss under correct specification.</p>

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Curvature-standardised Robust Score Estimation for the Gompertz Distribution

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  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Patchanok Srisuradetchai

摘要

Likelihood-based inference for exponential-tail lifetime models can be unstable in finite samples, particularly under right-tail contamination. We develop a robust score-based inference procedure for the two-parameter Gompertz distribution by constructing estimating equations in which influence is bounded after standardisation by the local curvature of the likelihood, implemented via OPG-whitened score contributions and Huber weighting. Uncertainty is quantified using a Godambe (sandwich) covariance estimator, enabling Wald-type inference. Simulation results under clean sampling and controlled right-tail contamination show improved stability and more reliable coverage than maximum likelihood, with a moderate efficiency loss under correct specification.