  The problem of preferential sampling in geostatistics arises when the choise
of location to be sampled is made with information about the phenomena in the
study. The geostatistical model under preferential sampling deals with this
problem, but parameter estimation is challenging because the likelihood
function has no closed form. We developed an MCEM and an SAEM algorithm for
finding the maximum likelihood estimators of parameters of the model and
compared our methodology with the existing ones: Monte Carlo likelihood
approximation and Laplace approximation. Simulated studies were realized to
assess the quality of the proposed methods and showed good parameter estimation
and prediction in preferential sampling. Finally, we illustrate our findings on
the well known moss data from Galicia.
