multiplying a Gaussian matrix and a Gaussian vector
This arXived note by Pierre-Alexandre Mattei was actually inspired by one of my blog entries, itself written from a resolution of a question on X validated. The original result about the Laplace distribution actually dates at least to 1932 and a paper by Wishart and Bartlett!I am not sure the construct has clear statistical implications, but it is nonetheless a good calculus exercise.
The note produces an extension to the multivariate case. Where the Laplace distribution is harder to define, in that multiple constructions are possible. The current paper opts for a definition based on the characteristic function. Which leads to a rather unsavoury density with Bessel functions. It however satisfies the constructive definition of being a multivariate Normal multiplied by a χ variate plus a constant vector multiplied by the same squared χ variate. It can also be derived as the distribution of
when W is a (p,q) matrix with iid Gaussian columns and y is a Gaussian vector with independent components. And μ is a vector of the proper dimension. When μ=0 the marginals remain Laplace.