In this paper, we respond to a critique of one of our papers previously published in this journal, entitled "TVOR: Finding Discrete Total Variation Outliers among Histograms". Our paper proposes a method for smoothness outliers detection among histograms by using the relation between their discrete total variations (DTV) and their respective sample sizes. In this response, we demonstrate point by point that, contrary to its claims, the critique has not found any mistakes or problems in our paper, either in the used datasets, methodology, or in the obtained top outlier candidates. On the contrary, the critique's claims can easily be shown to be mathematically unfounded, to directly contradict the common statistical theorems, and to go against well established demographic terms. Exactly this is done in the reply here by providing both theoretical and experimental evidence. Additionally, due to critique's complaint, a more extensive research on top outlier candidate, i.e. the Jasenovac list is conducted and in order to clear any of the critique's doubts, new evidence of its problematic nature unseen in other lists are presented. This reply is accompanied by additional theoretical explanations, simulations, and experimental results that not only confirm the earlier findings, but also present new data. The source code is at https://github.com/DiscreteTotalVariation/TVOR.
翻译:在本文中,我们回应了我们以前在本期刊上发表的一篇题为“TVOR:在直方图中找到分辨的完全变异外端”的论文的批评。我们的论文建议采用一种方法,利用直方图的离散总变异(DTV)和各自的抽样大小之间的关系,在直方图中顺利发现异常异常外端。在答复中,我们逐点表明,与我们的说法相反,批评在我们的论文中没有发现任何错误或问题,无论是在使用过的数据集、方法还是获得的顶尖外端候选人中。相反,批评的主张很容易在数学上被证明是没有根据的,直接与共同的统计理论理论相矛盾,并且与既定的人口学术语相反。这正是在答复中通过提供理论和实验证据来完成的。此外,由于批评,对顶端外端候选人进行了更为广泛的研究,即进行了Jasenovac名单,并且为了清除任何批评性的疑问,因此,批评性的说法很容易被显示为没有根据数学/com获得的新证据,因此,其早期的理论结果的新证据和其他数据来源也证实了。