> I was wondering if there are any R functions that give the tail area
> of a sum of chisquare distributions of the type:
> a_1 X_1 + a_2 X_2
> where a_1 and a_2 are constants and X_1 and X_2 are independent
> chi-square variables with different degrees of freedom.
You might also check out Welch and Satterthwaite's (separate) papers on
effective degrees of freedom for compound estimates of variance, which led to a
thing called the welch-satterthwaite equation by one (more or less notorious,
but widely used) document called the ISO Guide to Expression of Uncertainty in
Measurement (ISO, 1995). The original papers are
B. L. Welch, J. Royal Stat. Soc. Suppl.(1936) 3 29-48
B. L. Welch, Biometrika, (1938) 29 350-362
B. L. Welch, Biometrika, (1947) 34 28-35
F. E. Satterthwaite, Psychometrika (1941) 6 309-316
F. E. Satterthwaite, Biometrics Bulletin, (1946) 2 part 6 110-114
The W-S equation - which I believe is a special case of Welch's somewhat
more general treatment - says that if you have multiple independent estimated
variances v[i] (could be more or less equivalent to your a_i X_i?) with degrees
of freedom nu[i], the distribution of their sum is approximately a scaled
chi-squared distribution with effective degrees of freedom nu.effective given by
nu.effective = sum(v[i])^2 / sum( (v[i]^2)/nu[i] )
If I recall correctly, with an observed variance s^2 (corresponding to the
sum(v[i] above if those are observed varianes), nu*(s^2 /sigma^2) is distributed
as chi-squared with degrees of freedom nu, so the scaling factor for quantiles
would come out of there (depending whether you're after the tail areas for
s^2 given sigma^2 or for a confidence interval for sigma^2 given s^2)
However, I will be most interested to see what a more exact calculation
provides!
Steve Ellison
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