论文标题
大约有强大的置信度分布
On approximate robust confidence distributions
论文作者
论文摘要
置信分布是一种完整的工具，用于根据假定的参数模型对$ψ$的频繁推断$ψ$。的确，它允许达到点估计值，评估其精度，设置测试以及衡量“ $ψ>ψ_0$”或“ $ψ_1\ leq leqψ\ leqψ_2$”的陈述的衡量标准，以衍生置信区间，比较来自其他研究参数的其他参数等。 此贡献的目的是讨论从无偏的$ m $估计功能得出的稳健置信分布，当假定分布只是一个近似参数模型或观察到的数据中存在偏差值时，该函数为$ψ$提供了强大的推断。我们首先说明如何从可靠的置换量的渐近理论中得出强大的置信度分布，从而使基于似然的结果并扩展了可用于强大评分规则的结果。然后，我们通过仿真方法讨论鲁棒置信分布的推导。在非效率测试的背景下说明了一项应用程序和仿真研究，其中$ H_0：ψ\ leqψ_0$的零假设令人感兴趣。
A confidence distribution is a complete tool for making frequentist inference for a parameter of interest $ψ$ based on an assumed parametric model. Indeed, it allows to reach point estimates, to assess their precision, to set up tests along with measures of evidence for statements of the type "$ψ> ψ_0$" or "$ψ_1 \leq ψ\leq ψ_2$", to derive confidence intervals, comparing the parameter of interest with other parameters from other studies, etc. The aim of this contribution is to discuss robust confidence distributions derived from unbiased $M-$estimating functions, which provide robust inference for $ψ$ when the assumed distribution is just an approximate parametric model or in the presence of deviant values in the observed data. Paralleling likelihood-based results and extending results available for robust scoring rules, we first illustrate how robust confidence distributions can be derived from the asymptotic theory of robust pivotal quantities. Then, we discuss the derivation of robust confidence distributions via simulation methods. An application and a simulation study are illustrated in the context of non-inferiority testing, in which null hypotheses of the form $H_0: ψ\leq ψ_0$ are of interest.
