  Model selection methods are used in different scientific contexts to
represent a characteristic data set in terms of a reduced number of parameters.
Apparently, these methods have not found their way into the literature on
multibody systems dynamics. Multibody models can be considered parametric
models in terms of their dynamic parameters, and model selection techniques can
then be used to express these models in terms of a reduced number of
parameters. These parameter-reduced models are expected to have a smaller
computational complexity than the original one and still preserve the desired
level of accuracy. They are also known to be good candidates for parameter
estimation purposes.
  In this work, simulations of the actual model are used to define a data set
that is representative of the system's standard working conditions. A
parameter-reduced model is chosen and its parameter values estimated so that
they minimize the prediction error on these data. To that end, model selection
heuristics and normalized error measures are proposed.
  Using this methodology, two multibody systems with very different
characteristic mobility are analyzed. Highly considerable reductions in the
number of parameters and computational cost are obtained without compromising
the accuracy of the reduced model too much. As an additional result, a
generalization of the base parameter concept to the context of
parameter-reduced models is proposed.
