Inapplicability of the TVOR Method to USHMM Data Outlier Identification

TL;DR

This paper critically evaluates the TVOR method for outlier detection in histograms and demonstrates its inapplicability to USHMM Holocaust victim data due to dataset biases and assumption violations.

Contribution

It provides a detailed critique showing that the TVOR model's assumptions do not hold for the USHMM dataset, highlighting limitations in applying the method to real-world historical data.

Findings

TVOR model biases towards larger histograms

Dataset has sparse data points around the point of interest

Victims' age distributions vary significantly across lists

Abstract

Recent paper "TVOR: Finding Discrete Total Variation Outliers Among Histograms" [arXiv:2012.11574] introduces the Total Variation Outlier Recognizer (TVOR) method for identification of outliers among a given set of histograms. After providing a theoretical discussion of the method and verifying its success on synthetic and population census data, it applies the TVOR model to histograms of ages of Holocaust victims produced using United States Holocaust Memorial Museum data. It purports to identify the list of victims of the Jasenovac concentration camp as potentially suspicious. In this comment paper, we show that the TVOR model and its assumptions are grossly inapplicable to the considered dataset. When applied to the considered data, the model is biased in assigning a higher outlier score to histograms of larger sizes, the set of data points is extremely sparse around the point of interest, the dataset has not been reviewed to remove obvious data processing errors, and, contrary to the model requirements, the distributions of the victims' ages naturally vary significantly across victim lists.