  Interval-valued data receives much attention due to its wide applications in
the fields of finance, econometrics, meteorology and medicine. However, most
regression models developed for interval-valued data assume observations are
mutually independent, not adapted to the scenario that individuals are
spatially correlated. We propose a new linear model to accommodate to
areal-type spatial dependency existed in interval-valued data. Specifically,
spatial correlation among centers of responses are considered. To improve the
new model's prediction accuracy, we add three inequality constrains. Parameters
are obtained by an algorithm combining grid search technique and the
constrained least squares method. Numerical experiments are designed to examine
prediction performances of the proposed model. We also employ a weather dataset
to demonstrate usefulness of our model.
