On two-sided p-values for non-symmetric distributions

Small-sample one-sided testing in extreme value regression models

We derive adjusted signed likelihood ratio statistics for a general class of extreme value regression models. The adjustments reduce the error in the standard normal approximation to the distribution of the signed likelihood ratio statistic. We use Monte Carlo simulations to compare the finite-sample performance of the different tests. Our simulations suggest that the signed likelihood ratio test tends to be liberal when the sample size is not large, and that the adjustments are effective in shrinking the size distortion. Two real data applications are presented and discussed.

One-Sided Confidence About Functionals Over Tangent Cones

In the setup of i.i.d.~observations and a real valued differentiable functional~$T$, locally asymptotic upper bounds are derived for the power of one-sided tests (simple, versus large values of~$T$)and for the confidence probability of lower confidence limits (for the value of~$T$), in the case that the tangent set is only a convex cone. The bounds, and the tests and estimators which achieve the bounds, are based on the projection of the influence curve of the functional on the closed convex cone, as opposed to its closed linear span. The higher efficiency comes along with some weaker, only on…

自然指数族中的精确双侧p值：重合性、非唯一性与样本量稳定性

Exact two-sided p-values in natural exponential families: coincidence, non-uniqueness, and sample-size stability

该研究探讨自然指数族中精确双侧p值的非唯一性，分析不同构造p值的重合条件，结合相关定理刻画高斯族等特殊分布，并通过示例量化p值差异与决策分歧。

Iterated Jackknives and Two-Sided Variance Inequalities

We consider the variance of a function of $n$ independent random variables and provide new inequalities which, in particular, extend previous results obtained for symmetric functions in the i.i.d.~setting. For instance, we obtain various upper and lower variance bounds based on iterated jackknives statistics that can be considered as generalizations of the Efron-Stein inequality.

Locally most powerful sequential tests of a simple hypothesis vs. One-sided alternatives for independent observations

Let $X_1,X_2,..., X_n,...$ be a stochastic process with independent values whose distribution $P_θ$ depends on an unknown parameter $θ$, $θ\inΘ$, where $Θ$ is an open subset of the real line. The problem of testing $H_0:$ $θ=θ_0$ vs. a composite alternative $H_1:$ $θ>θ_0$ is considered, where $θ_0\inΘ$ is a fixed value of the parameter. The main objective of this work is the characterization of the structure of the locally most powerful (in the sense of Berk) sequential tests in this problem.

双向高斯均值检验中的依赖意识虚假发现率控制

Dependence-Aware False Discovery Rate Control in Two-Sided Gaussian Mean Testing

本文提出了一种基于正左尾依赖性的改进方法，用于在双向高斯均值检验中更有效地控制虚假发现率。