Modeling and prediction for multivariate spatial factor analysis
Modeling and prediction for multivariate spatial factor analysis
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DOI：
10.1016/s0378-3758(02)00173-8
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发表时间：
2003-08-01
影响因子：
0.9
通讯作者：
Amemiya, Y
中科院分区：
文献类型：
--
作者：
Christensen, WF;Amemiya, Y
考虑了多变量空间数据的因子分析。提出了一种系统的方法来模拟潜在不规则间隔的潜在结构，地理参考向量观测。讨论了选择因子数量和建立模型的统计推断程序。在不指定分布形式和因子协方差函数的情况下，我们导出了一个简单实用的推理程序有效的条件。本文还讨论了多变量预测问题，并介绍了一种将潜在变量建模与无测量误差克里格技术相结合的方法。给出了应用农业数据的仿真结果和算例。(C) 2002 Elsevier Science B.V.版权所有
Factor analysis of multivariate spatial data is considered. A systematic approach for modeling the underlying structure of potentially irregularly spaced, geo-referenced vector observations is proposed. Statistical inference procedures for selecting the number of factors and for model building are discussed. We derive a condition under which a simple and practical inference procedure is valid without specifying the form of distributions and factor covariance functions. The multivariate prediction problem is also discussed, and a procedure combining the latent variable modeling and a measurement-error-free kriging technique is introduced. Simulation results and an example using agricultural data are presented. (C) 2002 Elsevier Science B.V. All rights reserved.