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Mixture representations of noncentral distributions

With any symmetric distribution $μ$ on the real line we may associate a parametric family of noncentral distributions as the distributions of $(X+δ)^2$, $δ\not=0$, where $X$ is a random variable with distribution $μ$. The classical case arises if $μ$ is the standard normal distribution, leading to the noncentral chi-squared distributions. It is well-known that these may be written as Poisson mixtures of the central chi-squared distributions with odd degrees of freedom. We obtain such mixture representations for the logistic distribution and for the hyperbolic secant distribution. We also derive alternative representations for chi-squared distributions and relate these to representations of the Poisson family. While such questions originated in parametric statistics they also appear in the context of the generalized second Ray-Knight theorem, which connects Gaussian processes and local times of Markov processes.