Undergrad Citation needed: Only multivariate rotationally invariant distribution with iid components is a multivariate normal distribution

Click For Summary
The discussion centers on the proposition that a random vector with independent and identically distributed (iid) components and mean zero is only invariant under orthogonal transformations if its distribution is multivariate normal. The argument involves the probability density function (PDF) of the random vector, which must be even to satisfy invariance under orthogonal transformations. This leads to the conclusion that the corresponding symmetric matrix must be a multiple of the identity, resulting in a specific form for the PDF. The conversation references the Maxwell characterization of the multivariate normal distribution as a key source for this proposition. Ultimately, the thread concludes with a successful citation of the relevant article that supports the claim.
DrDu
Science Advisor
Messages
6,423
Reaction score
1,004
TL;DR
I need a citation for the proposition that the only multivariate rotationally invariant distribution with iid components is a multivariate normal distribution.
I need a citation for the following proposition: Assume a random vector ##X=(X_1, ..., X_n)^T## with iid components ##X_i## and mean 0, then the distribution of ##X## is only invariant with respect to orthogonal transformations, if the distribution of the ##X_i## is a normal distribution.
Thank you for your help!
 
Physics news on Phys.org
The PDF of X is <br /> f(x_1)\dots f(x_n) where f is the PDF of each X_i. Invariance under orthogonal transformations would require f to be even, since the transformation which multiplies the ith component by -1 and fixes the others is orthogonal. We can then write f(z) = g(z^2) whilst g(x_1^2) \cdots g(x_n^2) = F(x^TAx) for some symmetric matrix A which satisfies R^TAR = A for every orthogonal R. This is equivalent to the requiement that A should commute with every orthogonal R. I believe this in fact results in A being a multiple of the identity. If so, we have <br /> g(x_1^2) \cdots g(x_n^2) = F(x_1^2 + \dots + x_n^2) where the multiplier of the identity has been absorbed into F. Setting all but one of the x_i to be zero then shows that <br /> g(x_j^2)g(0)^{n-1} = F(x_j^2). Setting g = Ch where h(0) = 1 we find <br /> F = C^n h where <br /> h(z_1) \cdots h(z_n) = h(z_1 + \dots + z_n) for all (z_1, \dots, z_n) \in [0, \infty)^n. I think now we can proceed by induction on n, noting that for n = 2 and the assumption of continuous h we have h(z) = h(1)^z = \exp(z\log h(1)).
 
Look up the Maxwell characterization of the multivariate normal distribution.
 
First trick I learned this one a long time ago and have used it to entertain and amuse young kids. Ask your friend to write down a three-digit number without showing it to you. Then ask him or her to rearrange the digits to form a new three-digit number. After that, write whichever is the larger number above the other number, and then subtract the smaller from the larger, making sure that you don't see any of the numbers. Then ask the young "victim" to tell you any two of the digits of the...

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 6 ·
Replies
6
Views
2K
Replies
1
Views
4K
Replies
11
Views
13K
  • · Replies 25 ·
Replies
25
Views
3K
  • · Replies 6 ·
Replies
6
Views
2K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 4 ·
Replies
4
Views
2K
  • · Replies 5 ·
Replies
5
Views
8K