Introduces quantile-based effectiveness persistence function as tail mean divided by quantile, shows equivalence to first L-moment of scaled tail, and develops nonparametric estimator with bootstrap equivalence test for biosimilar evaluation.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
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.
Dynamic Vine Copulas detect time-varying higher-order interactions by contrasting full vines against their 1-truncated versions on held-out data, separating pairwise from conditional dependence contributions.
An adaptive jump test for discretely observed high-frequency semimartingales is constructed by merging the Aït-Sahalia-Jacod ratio statistic and Lee-Mykland extreme-return statistic with the Cauchy combination rule, yielding asymptotic independence and closed-form power under the continuous null.
citing papers explorer
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Quantile-Based Effectiveness Persistence Function: A Tail-Focused Metric with Theory, Estimation, and Application to Biosimilar Evaluation
Introduces quantile-based effectiveness persistence function as tail mean divided by quantile, shows equivalence to first L-moment of scaled tail, and develops nonparametric estimator with bootstrap equivalence test for biosimilar evaluation.
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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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Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
Dynamic Vine Copulas detect time-varying higher-order interactions by contrasting full vines against their 1-truncated versions on held-out data, separating pairwise from conditional dependence contributions.
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Adaptive Test for Jump
An adaptive jump test for discretely observed high-frequency semimartingales is constructed by merging the Aït-Sahalia-Jacod ratio statistic and Lee-Mykland extreme-return statistic with the Cauchy combination rule, yielding asymptotic independence and closed-form power under the continuous null.