论文标题
用于异质性分析的空间功能计数模型
A spatial functional count model for heterogeneity analysis in time
论文作者
论文摘要
空间曲线动态模型框架采用了时空对数高斯COX过程模型中计数的功能预测。我们的空间功能估计方法在时间上处理基于小波的异质性分析和空间中的光谱分析。具体而言，通过最小化数据基础的多尺度模型与空间频谱域中的相应候选者之间的信息差异或相对熵来实现模型拟合。在对数高斯空间自回归L2值过程（SARL2过程）的家族中进行了仿真研究，以说明所提出的空间功能估计器的渐近性能。我们将建模策略应用于呼吸道疾病死亡率的时空预测。
A spatial curve dynamical model framework is adopted for functional prediction of counts in a spatiotemporal log-Gaussian Cox process model. Our spatial functional estimation approach handles both wavelet-based heterogeneity analysis in time, and spectral analysis in space. Specifically, model fitting is achieved by minimising the information divergence or relative entropy between the multiscale model underlying the data and the corresponding candidates in the spatial spectral domain. A simulation study is carried out within the family of log-Gaussian Spatial Autoregressive l2-valued processes (SARl2 processes) to illustrate the asymptotic properties of the proposed spatial functional estimators. We apply our modelling strategy to spatiotemporal prediction of respiratory disease mortality.
