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Huber's weight parameter for Rodionov function #5
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First I'd like to say thanks a LOT for this bringing Rodionovs method to R, @alexhroom ! Without your work it would still be on the brink of oblivion due to the unmaintained excel macro before Then, I agree with @jatalah that it would be great if Huber's weight function could be added. Without the Huber function, the Rodionov's method is really sensitive to outliers as they are often found in real-world data. Many thanks |
@jatalah @tjgrabs sorry for little progress on this so far - most explanations of how Huber's weight is used is a little less than clear, and the work is being held up by me looking for a better & more mathematical explanation in the literature (e.g. Rodionov's description says that the outliers are handled using Huber's weight, but doesn't particularly explains where this fits into his original 2004 algorithm). furthermore, as I do not have Microsoft Office my access to his excel macro is limited. if anyone can provide a paper describing the algorithm with huber's weight added it would be a much faster task, otherwise it might take a while as I need to find the time to better digest the use of the parameter. |
Dear @alexhroom, ExcalMacroModule_Code_BaseShiftInMean.txt Apart from this, I tested rshifts on a data set, and noticed two things: 1. The results differ from Rodionovs Excel implementation, even after I use a large Huber parameter (which I think should result in ignoring outliers). 2. Two breakpoints appeared in adjacent years. This is not a behavior I would expect. The results from the Excel plugin shows only a single breakpoint in 1973 and not in 2000. (see attached image). |
EDIT: This issue should be fixed with rshift v2.20, which uses a Rust implementation that as far as I can tell gives results equivalent to Rodionov's Excel version. |
Hi @alexhroom,
Could you implement Huber's weight parameter in the Rodionov function as in Rodionov (2006)?
This function handles outliers based on Huber's weight function (Huber, 2005).
The implementation is described in https://www.beringclimate.noaa.gov/regimes/help3.html.
Many thanks,
Huber, P. J. (2005), Robust estimation of a location parameter, Annals Mathematical Statistics, 35, 73-101
Rodionov, S. N. (2006). The problem of red noise in climate regime shift detection. Geophysical Research Letters, 31, L12707.
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