Would you happen the memory complexity of this algorithm? I maintain an online machine learning library written in Python, called creme, where we implement online statistics. We have a generic onine algorithm for estimating quantiles, and so a specific algorithm for estimating medians would be welcome. I'm always on the lookout for such online algorithms.
The P^2 algorithm used in creme is interesting. For the median it would give a 2 sided median deviation. Maybe this could be changed slightly to be symmetric and give Median and MAD. I'll look into.
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[ 2.8 ms ] story [ 33.2 ms ] threadSee here for quantiles: https://stackoverflow.com/questions/1058813/on-line-iterator...
The P^2 algorithm used in creme is interesting. For the median it would give a 2 sided median deviation. Maybe this could be changed slightly to be symmetric and give Median and MAD. I'll look into.
Discussed in the previous post: http://anthonylloyd.github.io/blog/2016/10/21/MAD-Outliers
It's roughly the range around the median where half the sample is within (since you can do 2 sided MAD also).
http://davidmlane.com/hyperstat/A106993.html
Here's a SE discussion with people using the term inconsistently without even noticing. https://datascience.stackexchange.com/questions/42760/mad-vs...
It's absurd to use ambiguous acronyms like this.