  Evidence of jet precession in many galactic and extragalactic sources has
been reported in the literature. Much of this evidence is based on studies of
the kinematics of the jet knots, which depends on the correct identification of
the components to determine their respective proper motions and position angles
on the plane of the sky. Identification problems related to fitting procedures,
as well as observations poorly sampled in time, may influence the follow up of
the components in time, which consequently might contribute to a
misinterpretation of the data. In order to deal with these limitations, we
introduce a very powerful statistical tool to analyse jet precession: the
cross-entropy method for continuous multi-extremal optimisation. Only based on
the raw data of the jet components (right ascension and declination offsets
from the core), the cross-entropy method searches for the precession model
parameters that better represent the data. In this work we present a large
number of tests to validate this technique, using synthetic precessing jets
built from a given set of precession parameters. Aiming to recover these
parameters, we applied the cross-entropy method to our precession model,
varying exhaustively the quantities associated to the method. Our results have
shown that even in the most challenging tests, the cross-entropy method was
able to find the correct parameters within 1%-level. Even for a non-precessing
jet, our optimization method could point out successfully the lack of
precession.
