Journal article
Computationally efficient restricted maximum likelihood estimation of generalized covariance functions
Mathematical geology, Vol.21(7), pp.655-672
10/1989
DOI: 10.1007/BF00893314
Abstract
Computational aspects of the estimation of generalized covariance functions by the method of restricted maximum likelihood (REML) are considered in detail. In general, REML estimation is computationally intensive, but significant computational savings are available in important special cases. The approach taken here restricts attention to data whose spatial configuration is a regular lattice, but makes no restrictions on the number of parameters involved in the generalized covariance nor (with the exception of one result) on the nature of the generalized covariance function's dependence on those parameters. Thus, this approach complements the recent work of L. G. Barendregt (1987), who considered computational aspects of REML estimation in the context of arbitrary spatial data configurations, but restricted attention to generalized covariances which are linear functions of only two parameters. © 1989 International Association for Mathematical Geology.
Details
- Title: Subtitle
- Computationally efficient restricted maximum likelihood estimation of generalized covariance functions
- Creators
- Dale L. Zimmerman - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Mathematical geology, Vol.21(7), pp.655-672
- DOI
- 10.1007/BF00893314
- ISSN
- 0882-8121
- eISSN
- 1573-8868
- Language
- English
- Date published
- 10/1989
- Academic Unit
- Biostatistics; Statistics and Actuarial Science
- Record Identifier
- 9984257612502771
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