Definition of white noise vectors As is shown in wikipedia: Click [here] (http://en.wikipedia.org/wiki/White_noise#Mathematical_definitions)
A random vector (that is, a partially indeterminate process that produces vectors of real numbers) is said to be a white noise vector or white random vector if its components each have a probability distribution with zero mean and finite variance, and are statistically independent.
Here comes my question, does the vector is still white noise, if the variances of the components are not the same any more?
Thank you very much.

On the context of random processes, a process $X$ with an auto correlation function $R_X(t,s)=\sigma^2\delta(t-s)$ is considered a (wide sense) stationary white process. However, if $R_X(t,s)=\sigma^2(t)\delta(t-s)$ then the process is still considered white, though not stationary.
Translating this logic to random vectors, I guess the answer to your question is yes.
