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tT
发表于 2011-7-13 14:33 | 只看该作者

RMSE vs. standard error

error
RMSE is the square root of the mean squared error.

Standard Error of Estimate (SEE) = square root of sum of squares divided by n-k-1

So does RMSE= SEE?
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WarrenB1

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2#
发表于 2011-7-13 14:33 | 只看该作者
Way to confuse. Throw in a quant question, and stare at the blank faces of candidates.

By the way i'd think the answer to your question is NO.

SEE = std deviation of error terms.
SEE = sqrt(variance of error)
SEE = sqrt(SSE/n-k-1)

where as MSE = SSE/ n-k-1 <-- there is no square root here.
SSE = squared sum of all errors, or residual sum of errors.

SSE/n-k-1 is not equal to SEE.

By the way what is RMSE? seeing it for the first time.

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neil1234

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3#
发表于 2011-7-13 14:33 | 只看该作者
they are not the same thing, but closely related. RMSE is for the MEAN, not the total errors. it is the average error.

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needhelp1700

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4#
发表于 2011-7-13 14:33 | 只看该作者
RMSE is sqrt(MSE). Same thing as far as I can tell.

It's a tool used to gauge in-sample and out-fo-sample forecasting accuracy. Low RMSE relative to another model = better forecasting.

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Uranus08

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5#
发表于 2011-7-13 14:33 | 只看该作者
As is with SEE

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tarunajwani

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6#
发表于 2011-7-13 14:33 | 只看该作者
So it boils down to whether MSE = Sum of squares / n, or MSE = sum of squares / n-k-1. On an Anove table you will find MSS and the associated degrees of freedom is n-k-1.

I think denominator for MSE = n, denominator in the SEE is n-k-1 and that's my story.

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