Journal article
Computationally exploitable structure of covariance matrices and generalized covariance matrices in spatial models
Journal of statistical computation and simulation, Vol.32(1-2), pp.1-15
1989
DOI: 10.1080/00949658908811149
Abstract
Many spatial analyses based on random field models, as varied as kriging, likelihood-based estimation of autocovariance functions, and optimal design of spatial experiments, require the repeated evaluation of a covariance matrix V or a kth-order generalized covariance matrix K and its subsequent inversion. This is generally a formidable computing problem for moderate and large data sets. In this article, however, it is shown that under certain model assumptions, V (or K) possesses one of several types of patterned structure that, if exploited, can significantly reduce the computational burden of the analysis. These patterned structures are characterized, and their implications for matrix evaluation and inversion are considered. The usefulness of the results is illustrated with a soil pH data set. © 1989, Taylor & Francis Group, LLC. All rights reserved.
Details
- Title: Subtitle
- Computationally exploitable structure of covariance matrices and generalized covariance matrices in spatial models
- Creators
- D. L Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of statistical computation and simulation, Vol.32(1-2), pp.1-15
- Publisher
- Taylor and Francis
- DOI
- 10.1080/00949658908811149
- ISSN
- 0094-9655
- eISSN
- 1563-5163
- Language
- English
- Date published
- 1989
- Academic Unit
- Biostatistics; Statistics and Actuarial Science
- Record Identifier
- 9984257609802771
Metrics
4 Record Views