论文标题
部分可观测时空混沌系统的无模型预测
A Constrained Spatial Autoregressive Model for Interval-valued data
论文作者
论文摘要
由于其在金融，计量经济学，气象学和医学领域的广泛应用，间隔值数据受到了广泛的关注。但是，用于间隔值数据开发的大多数回归模型都假定观察值是相互独立的，而不是适应个人在空间相关的情况下。我们提出了一个新的线性模型，以适应间隔值数据中存在的面向空间依赖性。具体而言，考虑了响应中心之间的空间相关性。为了提高新模型的预测准确性，我们添加了三个不平等约束。参数是通过结合网格搜索技术和约束最小二乘法的算法获得的。数值实验旨在检查所提出模型的预测性能。我们还采用了一个天气数据集来证明我们的模型的有用性。
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.
