Zero Expectile Processes and Bayesian Spatial Regression
利用期望值概念引入一类具有非对称、亚高斯边际分布的新空间过程，并基于此构建空间回归模型，通过贝叶斯方法进行拟合与推断，在模拟和加州空气污染数据上优于传统高斯过程方法。
We introduce new classes of stationary spatial processes with asymmetric, sub-Gaussian marginal distributions using the idea of expectiles. We derive theoretical properties of the proposed processes. Moreover, we use the proposed spatial processes to formulate a spatial regression model for point-referenced data where the spatially correlated errors have skewed marginal distribution. We introduce a Bayesian computational procedure for model fitting and inference for this class of spatial regression models. We compare the performance of the proposed method with the traditional Gaussian process-based spatial regression through simulation studies and by applying it to a dataset on air pollution in California.