
The commutation matrix $K$ is defined as a square matrix containing only zeroes and ones. Its main properties are that it transforms vecA into vecA', and that it reverses the order of a Kronecker product. An analytic expression for $K$ is given and many further properties are derived. Subsequently, these properties are applied to some problems connected with the normal distribution. The expectation is derived of $\varepsilon' A\varepsilon\cdot\varepsilon' B\varepsilon\cdot\varepsilon'C\varepsilon$, where $\varepsilon \sim N(0, V)$, and $A, B, C$ are symmetric. Further, the expectation and covariance matrix of $x \otimes y$ are found, where $x$ and $y$ are normally distributed dependent variables. Finally, the variance matrix of the (noncentral) Wishart distribution is derived.
normal distribution, commutation matrix, Kronecker product, stochastic vectors, Stochastic vectors, chi square moments, matrix differentiation, covariance matrices, Basic linear algebra, Multivariate distribution of statistics, 15A69, Multilinear algebra, tensor calculus, variance matrix of Wishart distribution, Quadratic and bilinear forms, inner products, 62H99, stochastically dependent vectors, Stochastic matrices, expectations
normal distribution, commutation matrix, Kronecker product, stochastic vectors, Stochastic vectors, chi square moments, matrix differentiation, covariance matrices, Basic linear algebra, Multivariate distribution of statistics, 15A69, Multilinear algebra, tensor calculus, variance matrix of Wishart distribution, Quadratic and bilinear forms, inner products, 62H99, stochastically dependent vectors, Stochastic matrices, expectations
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