An approximate likelihood function of spatial correlation parameters
Journal of the Korean Statistical Society(2015)
摘要
Even under assumption of normality, likelihood-based inferences are often difficult for large and irregularly spaced spatial datasets. Exact calculations of the likelihood for a Gaussian spatial process observed in n locations require O ( n 3 ) operations. Instead of Whittle’s approximation to the Gaussian log likelihood for large spatial datasets, this paper introduces anapproximated likelihood function of spatial parameters based onthe correlogram, which involves no calculation of determinants and is computationally feasible. The proposed likelihood approximation method for spatial parameter is applied to the estimation of the spatial structure of changes in the average summer temperature based on 30 years of data by using an regional climate model (RCM) with a particular global climate model (GCM) boundary condition. The results verify the benefits and the performance of the proposed method.
更多查看译文
关键词
Approximated likelihood,Correlogram,Spatial likelihood,Spatial statistics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络