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tennican

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07-06-2011
03:06 PM

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MdAD, MAD, MdAPE, MAPE

MdAD => median absolute deviation

MAD => mean absolute deviation

MdAPE => median absolute percentage error

MAPE => median absolute percentage error

thanks, Scott

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Andrey_N_Intel

Employee

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07-07-2011
12:31 AM

126 Views

The present version of Intel MKL Summary Stats does not provide functionality for median/mean absolute deviation/percentage error.At the same time, the library has "elementary" building blocks for those estimates includingmean and median. Can you please briefly clarify

-are those estimates- "bottleneck" in your code (that is, your application can spendsignificant amount of time incomputation of those estimates depending on the problem size)?

- what are the typical dimensions you work with (dimension of random vector/number of observations/etc)?

Thanks in advance,

Andrey

tennican

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07-07-2011
09:38 AM

126 Views

Actually, the only one I really care about is the MdAD from which I can easily get MdAPE.

It would probably be more efficient to compute it at the same time as other statistics.

But, I can always calculate it in another step.

As a robust measure of spread, I consider MdAD to be the next most important summary stat after standard deviation.

Our software is applied to an incredibly broad array of datasets of which most are small or filtered. But, I'm planning for the future.

thanks, Scott

Andrey_N_Intel

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07-07-2011
10:11 PM

126 Views

Thanks for the additional details.

We wouldanalyze your requestto understand what could be done.

Best,

Andrey

Gennady_F_Intel

Moderator

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07-07-2011
11:15 PM

126 Views

mecej4

Black Belt

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07-07-2011
11:53 PM

126 Views

The selection algorithm for computing the median, for example, has O(N) complexity on average, but can degenerate to O(N

It would be fairly cheap for the library to compute an estimate of the median along with the other moments. For example, the median of 3 medians of 3 sampled triplets could provide such an estimate. Or, the median of a randomly chosen sample from the input array. In many cases, such an estimate may be all that is needed.

A user who wanted the true median could make a second call to the library with this estimated median, which could be used efficiently with the selection algorithm (or another algorithm that benefits from having an estimate of the median) to find the exact median. Normal users who have no interest in the median would not experience a noticeable loss of performance.

tennican

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07-08-2011
06:13 PM

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I said that computing the Median Absolute Deviation at the same time that you compute the Median might be more efficient.

I was NOT referring to the computation of the mean or any other moment.

mecej4

Black Belt

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07-09-2011
12:11 AM

126 Views

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