So-called linear rank statistics provide a means for distribution-free (even in finite samples), yet highly flexible, two-sample testing in the setting of univariate random variables. Their flexibility derives from a choice of weights that can be adapted to any given (simple) alternative hypothesis to achieve efficiency in case of correct specification of said alternative, while their non-parametric nature guarantees well-calibrated $p$-values even under misspecification. By drawing connections to (generalized) maximum likelihood estimation, and exploiting recent work on ranks in multiple dimensions, we extend linear rank statistics both to multivariate random variables and composite alternatives. Doing so yields non-parametric, multivariate two-sample tests that mirror efficiency properties of likelihood ratio tests, while remaining robust against model misspecification. We prove non-parametric versions of the classical Wald and score tests facilitating hypothesis testing in the asymptotic regime, and relate these generalized linear rank statistics to linear spacing statistics enabling exact $p$-value computations in the small to moderate sample setting. Moreover, viewing rank statistics through the lens of likelihood ratios affords applications beyond fully efficient two-sample testing, of which we demonstrate three: testing in the presence of nuisance alternatives, simultaneous detection of location and scale shifts, and $K$-sample testing.
翻译:所谓的线性等级统计为无分布性统计(即使是有限的样本)提供了一种手段,但在设定单体随机变数时,这种高度灵活、两样样的测试非常灵活。其灵活性来自对重量的选择,这种选择可以适应任何给定(简单)替代假设,以便在正确指定上述替代物的情况下实现效率,而其非参数性能保证了合理校准的美元-价值,即便在区分不当的情况下也是如此。我们通过与(普遍)最大可能性估计相联系,并利用最近在多个层面的排名上的工作,将线性等级统计扩大到多变随机变量和复合替代物。这样可以产生非参数、多变的双样测试,反映概率比值测试的效率特性,同时保持与模型性格误差的强。我们证明古典沃尔德和得分测试的非参数版本有利于在淡体制度下进行假设性测试,并将这些一般线性等级统计与线性间隔统计联系起来,从而能够在中小样本设置中精确的美元值计算。此外,通过概率比率的镜像来查看等级统计,反映概率比值,反映概率比值的概率比值测试位置,比值比值测试比值比值比值比值比值比值比值比值比值比值比值,比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值,比值比值比值比值比值比值比值比值,比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值,比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值比值