A Characterization of the Normal Distribution by the Independence of a Pair of Random Vectors

Abstract: Kagan and Shalaevski 1967 have shown that if the random variables $X_1,\dots,X_n$ are independent and identically distributed and the distribution of $\sum_{i=1}^n(X_i+a_i)^2$ $a_i\in \mathbb{R}$ depends only on $\sum_{i=1}^na_i^2$ , then each $X_i$ follows the normal distribution $N(0, \sigma)$. Cook 1971 generalized this result replacing independence of all $X_i$ by the independence of $(X_1,\dots, X_m) \textrm{ and } (X_{m+1},\dots,X_n )$ and removing the requirement that $X_i$ have the same distribution. In this paper, we will give other characterizations of the normal distribution which are formulated in a similar spirit.