Semiparametric Estimation and Selection for Nonstationary Spatial Covariance Functions

doi:10.1198/jcgs.2010.07157

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/69235

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/69235

题名:	Semiparametric Estimation and Selection for Nonstationary Spatial Covariance Functions
作者:	張雅梅;徐南蓉;黃信誠
贡献者:	淡江大學統計學系
日期:	2010-03-01
上传时间:	2011-10-23 16:41:28 (UTC+8)
摘要:	We propose a method for estimating nonstationary spatial covariance functions by representing a spatial process as a linear combination of some local basis functions with uncorrelated random coefficients and some stationary processes, based on spatial data sampled in space with repeated measurements. By incorporating a large collection of local basis functions with various scales at various locations and stationary processes with various degrees of smoothness, the model is flexible enough to represent a wide variety of nonstationary spatial features. The covariance estimation and model selection are formulated as a regression problem with the sample covariances as the response and the covariances corresponding to the local basis functions and the stationary processes as the predictors. A constrained least squares approach is applied to select appropriate basis functions and stationary processes as well as estimate parameters simultaneously. In addition, a constrained generalized least squares approach is proposed to further account for the dependencies among the response variables. A simulation experiment shows that our method performs well in both covariance function estimation and spatial prediction. The methodology is applied to a U.S. precipitation dataset for illustration. Supplemental materials relating to the application are available online.
關聯:	Journal of Computational ; Graphical Statistics 19, pp.117-139
DOI:	10.1198/jcgs.2010.07157
显示于类别:	[統計學系暨研究所] 期刊論文

文件中的档案:

档案	大小	格式	浏览次数
index.html	0Kb	HTML	67	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....