Local intrinsic stationarity and its inference
针对密集空间数据，提出一种允许协方差平稳成分随位置变化的局部内在随机函数概念，并基于网格数据给出非参数估计方法及固定域渐近理论。
Dense spatial data are commonplace nowadays, and they provide the impetus for addressing nonstationarity in a general way. This paper extends the notion of intrinsic random function by allowing the stationary component of the covariance to vary with spatial location. A nonparametric estimation procedure based on gridded data is introduced for the case where the covariance function is regularly varying at any location. An asymptotic theory is developed for the procedure on a fixed domain by letting the grid size tend to zero.