We study the existence, strong consistency and asymptotic normality of\nestimators obtained from estimating functions, that are p-dimensional\nmartingale transforms. The problem is motivated by the analysis of evolutionary\nclustered data, with distributions belonging to the exponential family, and\nwhich may also vary in terms of other component series. Within a\nquasi-likelihood approach, we construct estimating equations, which accommodate\ndifferent forms of dependency among the components of the response vector and\nestablish multivariate extensions of results on linear and generalized linear\nmodels, with stochastic covariates. Furthermore, we characterize estimating\nfunctions which are asymptotically optimal, in that they lead to confidence\nregions for the regression parameters which are of minimum size,\nasymptotically. Results from a simulation study and an application to a real\ndataset are included.\n