  The Student-$t$ distribution is widely used in statistical modeling of
datasets involving outliers since its longer-than-normal tails provide a robust
approach to hand such data. Furthermore, data collected over time may contain
censored or missing observations, making it impossible to use standard
statistical procedures. This paper proposes an algorithm to estimate the
parameters of a censored linear regression model when the regression errors are
autocorrelated and the innovations follow a Student-$t$ distribution. To fit
the proposed model, maximum likelihood estimates are obtained throughout the
SAEM algorithm, which is a stochastic approximation of the EM algorithm useful
for models in which the E-step does not have an analytic form. The methods are
illustrated by the analysis of a real dataset that has left-censored and
missing observations. We also conducted two simulations studies to examine the
asymptotic properties of the estimates and the robustness of the model.
