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It seems to me the current formula in the article might be slightly incorrect. Here is the current formula:
Specifically, the current numerator suggests it would include each person's difference from themself, i.e., , where . However, if is defined as the set of all other persons who aren't , then the upper limit of 's index ought to be . Thus, I believe the numerator ought to be:
Moreover, I propose that the article be updated to explicitly define .
Lastly, I'm not sure whether the denominator needs any updating.
MosesRivera100 (
talk) 19:07, 7 September 2023 (UTC)reply
Using same projection for wealth and income Gini maps
Using the the same projection would allow for easy comparisons between wealth and income Ginis
Mrsmrmrmrs (
talk) 13:55, 18 January 2024 (UTC)reply
What I think should be changed: Although both Gini coefficients are defined as areas between certain curves and share certain properties, there is no simple direct relationship between the Gini coefficient of statistical dispersion and the Gini coefficient of a classifier.
Why it should be changed: Both Gini coefficients are defined as areas between certain curves, and in the case of known ratio of "event" to "non-event" in the data there is a simple relationship between them. Gt = Gn/(1+α). Gt is the traditional measure of statistical dispersion, Gn is the new measure for binary classifier, and α is the ratio between events to non events in the data.
Not done:Rogmike, source displays the following: SSRN Search Results
This paper has been removed from SSRN at the request of the author, SSRN, or the rights holder.
ARandomName123 (
talk)Ping me! 01:45, 4 March 2024 (UTC)reply
ARAndomName123,
Thank you for looking into my minor edit. the article I put on SSRN was taken out due to formality (they need my name on the first page of the PADF file). I will resubmit it shortly. If you prefer I can send it to you directly, so you can check the formulas (rather tedious but elementary).