  The starting point of this article is the question "How to retrieve
fingerprints of rhythm in written texts?" We address this problem in the case
of Brazilian and European Portuguese. These two dialects of Modern Portuguese
share the same lexicon and most of the sentences they produce are superficially
identical. Yet they are conjectured, on linguistic grounds, to implement
different rhythms. We show that this linguistic question can be formulated as a
problem of model selection in the class of variable length Markov chains. To
carry on this approach, we compare texts from European and Brazilian
Portuguese. These texts are previously encoded according to some basic rhythmic
features of the sentences which can be automatically retrieved. This is an
entirely new approach from the linguistic point of view. Our statistical
contribution is the introduction of the smallest maximizer criterion which is a
constant free procedure for model selection. As a by-product, this provides a
solution for the problem of optimal choice of the penalty constant when using
the BIC to select a variable length Markov chain. Besides proving the
consistency of the smallest maximizer criterion when the sample size diverges,
we also make a simulation study comparing our approach with both the standard
BIC selection and the Peres-Shields order estimation. Applied to the linguistic
sample constituted for our case study, the smallest maximizer criterion assigns
different context-tree models to the two dialects of Portuguese. The features
of the selected models are compatible with current conjectures discussed in the
linguistic literature.
