  Novel significance tests are proposed for the quite general additive
concurrent model formulation without the need of model, error structure
preliminary estimation or the use of tuning parameters. Making use of the
martingale difference divergence coefficient, we propose new tests to measure
the conditional mean independence in the concurrent model framework taking
under consideration all observed time instants. In particular, global
dependence tests to quantify the effect of a group of covariates in the
response as well as partial ones to apply covariates selection are introduced.
Their asymptotic distribution is obtained on each case and a bootstrap
algorithm is proposed to compute its p-values in practice. These new procedures
are tested by means of simulation studies and some real datasets analysis.
