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[ascl:2302.006] RCR: Robust Chauvenet Outlier Rejection

RCR provides advanced outlier rejection that is easy to use. Both sigma clipping, the simplest form of outlier rejection, and traditional Chauvenet rejection make use of non-robust quantities, the mean and standard deviation, which are sensitive to the outliers that they are being used to reject. This limits such techniques to samples with small contaminants or small contamination fractions. RCR instead first makes use of robust replacements for the mean, such as the median and the half-sample mode, and similar robust replacements for the standard deviation. RCR has been carefully calibrated and can be applied to samples with both large contaminants and large contaminant fractions (sometimes in excess of 90% contaminated).

Code site:
https://github.com/nickk124/robust-outlier-rejection https://skynet.unc.edu/rcr
Described in:
https://ui.adsabs.harvard.edu/abs/2023arXiv230107838K https://ui.adsabs.harvard.edu/abs/2018ApJS..238....2M
Bibcode:
2023ascl.soft02006K

Views: 808

ascl:2302.006
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