Multiscale variance stabilization via maximum likelihood

This article proposes maximum likelihood approaches for multiscale variance stabilization transformations for independently and identically distributed data. For two multiscale variance stabilization transformations we present new unified theoretical results on their Jacobians, a key component of the likelihood. The results provide a deeper understanding of the transformations and the ability to compute the likelihood in linear time. The transformations are shown empirically to compare favourably to the Box-Cox transformation.