Al Chou wrote:
>  mdiggory@latte.harvard.edu wrote:
>
>>Phil Steitz wrote:
>>
>>>Phil Steitz wrote:
>>>
>>>
>>>>mdiggory@latte.harvard.edu wrote:
>>>>
>>>>
>>>>>Phil Steitz wrote:
>>>>>
>>>>>
>>>>>>Since xbar = sum/n, the change has no impact on the which sums are
>>>>>>computed or squared. Instead of (sum/n)*(sum/n)*n your change just
>>>>>>computes sum**2/n. The difference is that you are a) eliminating
>>>>>>one division by n and one multiplication by n (no doubt a good
>>>>>>thing) and b) replacing direct multiplication with pow(,2). The
>>>>>>second of these used to be discouraged, but I doubt it makes any
>>>>>>difference with modern compilers. I would suggest collapsing the
>>>>>>denominators and doing just one cast  i.e., use
>>>>>>
>>>>>>(1) variance = sumsq  sum * (sum/(double) (n * (n  1)))
>>>>>>
>>>>>>If
>>>>>>
>>>>>>(2) variance = sumsq  (sum * sum)/(double) (n * (n  1))) or
>>>>>>
>>>>>>(3) variance = sumsq  Math.pow(sum,2)/(double) (n * (n  1))) give
>>>>>>
>>>>>>better accuracy, use one of them; but I would favor (1) since it
>>>>>>will be able to handle larger positive sums.
>>>>>>
>>>>>>I would also recommend forcing getVariance() to return 0 if the
>>>>>>result is negative (which can happen in the right circumstances for
>>>>>>any of these formulas).
>>>>>>
>>>>>>Phil
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>collapsing is definitely good, but I'm not sure about these
>>>>>equations, from my experience, approaching (2) would look something
>>>>>more like
>>>>>
>>>>>variance = (((double)n)*sumsq  (sum * sum)) / (double) (n * (n  1));
>>>>>
>>>>>see (5) in http://mathworld.wolfram.com/kStatistic.html
>>>>
>>>>
>>>>
>>>>That formula is the formula for the 2nd kstatistic, which is *not*
>>>>the same as the sample variance. The standard formula for the sample
>>>>variance is presented in equation (3) here:
>>>>http://mathworld.wolfram.com/SampleVariance.html or in any elementary
>>>>statistics text. Formulas (1)(3) above (and the current
>>>>implementation) are all equivalent to the standard defintion.
>>>>
>>>>What you have above is not. The relation between the variance and the
>>>>second kstatistic is presented in (9) on
>>>>http://mathworld.wolfram.com/kStatistic.html
>>>
>>>
>>>I just realized that I misread Wolfram's definitions. What he is
>>>defining as the 2nd kstatistic is the correct formula for the sample
>>>variance. I am also missing some parenthesis above. Your formula is
>>>correct. Sorry.
>>>
>>>Phil
>>>
>>
>>No problem, I just wanted to make sure we're all on the same page, there are
>>always many times I am wrong too.
>>
>>Thanks for double checking,
>>Mark
>
>
>
>
> Uh, did we drop the idea of using the "corrected twopass" algorithm for the
> variance in the nonrolling case? I excerpted that thread below.
I was going to mention that. The discussion above regards the
"nonstored vector" approach (UnivariateImpl). I submitted some patches
to the "stored" classes (StoredUnivariate,AbstractStoreUnivariate) last
weekend (adding percentiles 
http://issues.apache.org/bugzilla/show_bug.cgi?id=20377) and I was
waiting for them to get committed before suggesting this
change/submitting the patch.
Given our recent NR entanglements, we need to make sure that if and when
we apply that patch we document using the original source of the formula.
Thanks for the remider.
What is relevant to the UnivariateImpl computations is Brent's
suggestion re: making "running" error corrections. Have you
investigated this further?
>
> Al
>
>
> Date: Mon, 26 May 2003 18:28:45 0700 (PDT)
> From: Al Chou <hotfusionman@yahoo.com>
> Subject: [math] rolling formulas for statistics (was RE: Greetings from a
> newcomer (b
>
>  Phil Steitz <phil@steitz.com> wrote:
>
>>Al Chou wrote:
>>
>>> Phil Steitz <phil@steitz.com> wrote:
>>>
>>>
>>>>Brent Worden wrote:
>>>>
>>>>>>Mark R. Diggory wrote:
>>>>>
>>>>>There are easy formulas for skewness and kurtosis based on the central
>>>>>moments which could be used for the stored, univariate implementations:
>>>>>http://mathworld.wolfram.com/Skewness.html
>>>>>http://mathworld.wolfram.com/Kurtosis.html
>>>>>
>>>>>As for the rolling implementations, there might be some more research
>>>>>involved before using this method because of their memoryless property.
>>>>
>>>>But
>>>>
>>>>>for starters, the sum and sumsq can easily be replaced with there central
>>>>>moment counterparts, mean and variance. There are formulas that update
>>>>
>>>>those
>>>>
>>>>>metrics when a new value is added. Weisberg's "Applied Linear Regression"
>>>>>outlines two such updating formulas for mean and sum of squares which
are
>>>>>numerically superior to direct computation and the raw moment methods.
>>>>
>>>>Why exactly, are these numerically superior? For what class of
>>>>examples? Looks like lots more operations to me, especially in the
>>>>UnivariateImpl case where the mean, variance are computed only when
>>>>demanded  i.e., there is no requirement to generate mean[0],
>>>>mean[1],...etc. I understand that adding (or better, swapping)
>>>>operations can sometimes add precision, but I am having a hard time
>>>>seeing exactly where the benefit is in this case, especially given the
>>>>amount of additional computation required.
>>>
>>>
>>>_Numerical Recipes in C_, 2nd ed. p. 613
>>>(http://libwww.lanl.gov/numerical/bookcpdf/c141.pdf) explains:
>>>"Many textbooks use the binomial theorem to expand out the definitions [of
>>>statistical quantities] into sums of various powers of the data, ...[,] but
>>>this can magnify the roundoff error by a large factor... . A clever way to
>>>minimize roundoff error, especially for large samples, is to use the
>>
>>corrected
>>
>>>twopass algorithm [1]: First calculate x[bar, the mean of x], then
>>
>>calculate
>>
>>>[formula for variance in terms of x  xbar.] ..."
>>>
>>>[1] "Algorithms for Computing the Sample Variance: Analysis and
>>>Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. 1983, American
>>>Statistician, vol. 37, pp. 242?247.
>>
>>Thany you, Al!
>>
>>I am convinced by this for that at least for the variance and higher
>>order moments, whenever we actually store the values (that would include
>>UnivariateImpl with finite window), we should use the "corrected one [sic]
>>pass" formula (14.1.8) from
>>http://libwww.lanl.gov/numerical/bookcpdf/c141.pdf. It is a clever
>>idea to explicitly correct for error in the mean.
>
>
> =====
> Albert Davidson Chou
>
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