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.

翻译：不同价值数据因其在金融、计量经济学、气象学和医学领域的广泛应用而得到很大重视,然而,为不同价值数据开发的大多数回归模型假定观测是相互独立的,不适应个人在空间上相互关联的设想;我们提议一种新的线性模型,以适应在不同价值数据中存在的不同类型空间依赖性;具体地说,考虑各反应中心之间的空间相关性;为了提高新模型的预测准确性,我们增加了三个不平等制约因素;参数是通过将电网搜索技术和受限制的最低平方法相结合的算法获得的;数字实验旨在审查拟议模型的预测性能;我们还利用气象数据集来显示我们模型的有用性。