  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. The method relies on comparing the smoothness of each given
histogram, given by its discrete total variation, to those of other histograms
in the dataset, with the underlying assumption that most histograms in the data
set should be of similar smoothness. The paper concludes by applying the TVOR
model to histograms of ages of Holocaust victims produced using United States
Holocaust Memorial Museum (USHMM) data, and purports to identify the list of
victims of the Jasenovac concentration camp as potentially suspicious. In this
paper, we show that the TVOR model and its assumptions are grossly inapplicable
to the considered dataset. Namely, the dataset does not satisfy the model's
critical assumption of the shared smoothness between distributions of the
victims' ages across lists, the model is biased in assigning a higher outlier
score to histograms of larger sizes, and the dataset has not been reviewed to
remove obvious data processing errors, leading to duplication of hundreds of
thousands of entries when performing the data analysis.
