Pete Rawson wrote:
Gentlemen,
You've hit a topic I've become more confused about after
researching some of the original papers on this subject.
Here are a few questions which I would like to become
educated about.
1) Will the calculated results of ADEV, ODEV, MDEV & TOTDEV
suggest which result applies best to the data being analyzed?
2) What attributes of the data to be analyzed suggest which
computation is most appropriate?
3) Will some computed results indicate that the analysis is NOT
appropriate? (Are false results obvious?)
There are two things to keep in mind, the bias and the error bars.
Some of these estimators produces biased values as a result of the
dominant noise source. You need to identify the dominant noise source
(use the lag 1 autocorrelation noise identification, almost trivial to
perform). Then with the dominant noise source identified the bias can be
determined.
Error bars will high-light in which area of the graph where a particular
estimator has problems. Comparing the spread of the error-bars between
various estimators allow for identifying which is best for the task.
Look at TOTAL and Theo variants.
Error bars is essentially a reformulation of the Equivalent Degrees of
Freedom (EDF) and EDF change quite drastically with m. Comparison
between different measurements can be done on EDF for m, and highest EDF
wins. It's a measurement on how well the data in the sequence is used by
the estimator.
I'm sure there are more aspects worth learning than these, but
they might serve to get a conversation underway.
Any enlightenment would be greatly appreciated.
This has been the point of the exercise... spreading the knowledge.
I am digging too... but the little stuff I have picked up could probably
be good knowledge to others, so I stirred the pot a little.
Cheers,
Magnus
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