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多维分布函数、分位数函数、秩与符号：测度运输方法AI 解读

Unlike the real line, the real space Rd, for d≥2, is not canonically ordered. As a consequence, such fundamental univariate concepts as quantile and distribution functions and their empirical counterparts, involving ranks and signs, do not canonically extend to the multivariate context. Palliating that lack of a canonical ordering has been an open problem for more than half a century, generating an abundant literature and motivating, among others, the development of…

修剪与似然：椭圆模型中的稳健位置及离散程度估计AI 解读

Robust estimators of location and dispersion are often used in the elliptical model to obtain an uncontaminated and highly representative subsample by trimming the data outside an ellipsoid based in the associated Mahalanobis distance. Here we analyze some one (or k)-step Maximum Likelihood Estimators computed on a subsample obtained with such a procedure. We introduce different models which arise naturally from the ways in which the discarded data can be treated, leading to…

分布的修剪比较AI 解读

This article introduces an analysis of similarity of distributions based on the L2-Wasserstein distance between trimmed distributions. Our main innovation is the use of the impartial trimming methodology, already considered in robust statistics, which we adapt to this setup. Instead of simply removing data at the tails to provide some robustness to the similarity analysis, we develop a data-driven trimming method aimed at maximizing similarity between distributions.…

基于 L₂-Wasserstein 距离的拟合优度检验AI 解读

We consider the Wasserstein distance between a sample distribution and the set of normal distributions as a measure of nonnormality. By considering the standardized version of this distance we obtain a version of Shapiro–Wilk’s test of normality. The asymptotic behavior of the statistic is studied using approximations of the quantile process by Brownian bridges. This method differs from the “ad hoc” method of de Wet and Venter and permits a similar analysis for testing other…

多元广义修剪 k 均值的中心极限定理AI 解读

A central limit theorem for generalized trimmed $k$-means is obtained in a very general framework that covers the multivariate setting, general penalty functions and general $k \geq 1$. Several applications, including the location estimator case $(k = 1)$ for elliptical distributions and the construction of multivariate (not necessarily connected) tolerance zones, are also given.