Estimating basis functions in massive fields under the spatial mixed effects model

Statistical Analysis and Data Mining

article

Karl Pazdernik¹  Ranjan Maitra³ 

[1] National Security Directorate, Pacific Northwest National Laboratory;Department of Statistics, North Carolina State University;Department of Statistics, Iowa State University

关键词: alternating expectation conditional maximization algorithm;    bandwidth;    basis functions;    fixed rank kriging;    maximum likelihood estimation;    range parameter;   

DOI  :  10.1002/sam.11537

学科分类：社会科学、人文和艺术（综合）

来源: John Wiley & Sons, Inc.

【 摘 要 】

Spatial prediction is commonly achieved under the assumption of a Gaussian random field by obtaining maximum likelihood estimates of parameters, and then using the kriging equations to arrive at predicted values. For massive datasets, fixed rank kriging using the expectation–maximization algorithm for estimation has been proposed as an alternative to the usual but computationally prohibitive kriging method. The method reduces computation cost of estimation by redefining the spatial process as a linear combination of basis functions and spatial random effects. A disadvantage of this method is that it imposes constraints on the relationship between the observed locations and the knots. We develop an alternative method that utilizes the spatial mixed effects model, but allows for additional flexibility by estimating the range of the spatial dependence between the observations and the knots via an alternating expectation conditional maximization algorithm. Experiments show that our methodology improves estimation without sacrificing prediction accuracy while also minimizing the additional computational burden of extra parameter estimation. The methodology is applied to a temperature dataset archived by the United States National Climate Data Center, with improved results over previous methodology.

【 授权许可】

Unknown