I know that the techniques as principal component analysis, factor analysis
or canonical correlation analysis are called R-techniques, because the
correlation matrix R plays an important role in this approach.
Instead techniques such as discriminant analysis, cluster analysis or
multidimensional s
Referring your example:
variance = 2_nd moment - (1_st moment),
that is:
2_nd moment = 0^2 * 0.2 + 1^2 * 0.3 + 2^2 * 0.2 + 3^2 * 0.2 + 4^2 * 0.1 =
4.5
1_st moment = 0 * 0.2 + 1 * 0.3 + 2 * 0.2 + 3 * 0.2 + 4 * 0.1 = 1.7
then
variance = 4.5 - (1.7)^2 = 1.61
then
standard deviation = sqrt(1.61) = 1.
Sorry,
the exact formula is V=(x-mu)' sigma^{-1}(y-mu).
However I have understand what you mean.
Thank you,
Paolo
"Herman Rubin" <[EMAIL PROTECTED]> ha scritto nel messaggio
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Hi,
let x, y be two i.i.d. N(mu, sigma) p_variate.
How can I show that the distribution of V=(x-mu)' sigma^{-1}(x-mu), where
sigma^{-1} is the inverse of co_variance matirx sigma, is symmetric?
Thanks,
Any help would be greatly appreciated.
Paolo
