2020
DOI: 10.1080/02664763.2020.1808599
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Robust analogs to the coefficient of variation

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Cited by 87 publications
(53 citation statements)
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“…Despite data clean-up, single cell data can contain spurious outlier observations, particularly in genetic perturbation experiments. To estimate the population variability, we therefore used a robust analogue of the coefficient of variation given by RCV M = 1.4826 × MAD / median, where MAD is the median absolute deviation, given by MAD ( x ) = median(| x - m |) where m = median ( x ) (Arachchige et al, 2020).…”
Section: Star Methodsmentioning
confidence: 99%
“…Despite data clean-up, single cell data can contain spurious outlier observations, particularly in genetic perturbation experiments. To estimate the population variability, we therefore used a robust analogue of the coefficient of variation given by RCV M = 1.4826 × MAD / median, where MAD is the median absolute deviation, given by MAD ( x ) = median(| x - m |) where m = median ( x ) (Arachchige et al, 2020).…”
Section: Star Methodsmentioning
confidence: 99%
“…This simulation is performed for all uncertainty sources, namely: invasion density, curve assignment, age, streamflow reduction curves, additional water availability and precipitation. For each of these scenarios, we summarize the total uncertainty in estimated streamflow reduction at the catchment and pixel level using a robust estimate of dispersion ‐ the median absolute deviation ( MAD ) ‐ and the robust coefficient of variation ( RCV M ) calculated as RCVM=1.4826italicMAD/M where MAD is the median absolute deviation and M median of streamflow reduction estimates across samples (Arachchige et al, 2020).…”
Section: Methodsmentioning
confidence: 99%
“…where MAD is the median absolute deviation and M median of streamflow reduction estimates across samples (Arachchige et al, 2020).…”
Section: Uncertainty Partitioningmentioning
confidence: 99%
“…For example, it is well known that travel times often are susceptible to outliers. There is a rich literature on metrics that are robust to outliers, and these would be natural candidates for new TTR metrics (Arachchige et al 2020;Rousseeuw and Hubert 2011;Spiegelman et al 2011).…”
Section: Proposal For Future Ttr Analysesmentioning
confidence: 99%