Estimation and Model Identification for Continuous Spatial Processes
针对连续域空间过程，提出了一种形式化的参数估计和模型识别方法，利用椭圆等高线二维有理谱密度函数建模空间协方差，并通过迭代估计减轻非格点数据最大似然估计的计算困难，适用于地下水等实际数据。
SUMMARY Formal parameter estimation and model identification procedures for continuous domain spatial processes are introduced. The processes are assumed to be adequately described by a linear model with residuals that follow a second-order stationary Gaussian random field and data are assumed to consist of noisy observations of the process at arbitrary sampling locations. A general class of two-dimensional rational spectral density functions with elliptic contours is used to model the spatial covariance function. An iterative estimation procedure alleviates many of the computational difficulties of conventional maximum likelihood estimation for non-lattice data. The procedure is applied to several generated data sets and to an actual ground-water data set.