Journal article
Approximate Likelihood for Large Irregularly Spaced Spatial Data
Journal of the American Statistical Association, Vol.102(477), pp.321-331
0
2007
DOI: 10.1198/016214506000000852
PMCID: PMC2601654
PMID: 19079638
Abstract
Likelihood approaches for large, irregularly spaced spatial datasets are often very difficult, if not infeasible, to implement due to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at n locations requires O(n 3 ) operations. We present a version of Whittle's approximation to the Gaussian log-likelihood for spatial regular lattices with missing values and for irregularly spaced datasets. This method requires O(nlog 2 n) operations and does not involve calculating determinants. We present simulations and theoretical results to show the benefits and the performance of the spatial likelihood approximation method presented here for spatial irregularly spaced datasets and lattices with missing values. We apply these methods to estimate the spatial structure of sea surface temperatures using satellite data with missing values.
Details
- Title: Subtitle
- Approximate Likelihood for Large Irregularly Spaced Spatial Data
- Creators
- Montserrat Fuentes - University of Iowa, Provost Office Administration
- Resource Type
- Journal article
- Publication Details
- Journal of the American Statistical Association, Vol.102(477), pp.321-331
- Event
- 0
- DOI
- 10.1198/016214506000000852
- PMID
- 19079638
- PMCID
- PMC2601654
- NLM abbreviation
- J Am Stat Assoc
- ISSN
- 0162-1459
- eISSN
- 1537-274X
- Publisher
- Taylor & Francis
- Language
- English
- Date published
- 2007
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Provost Office Administration
- Record Identifier
- 9983756684402771
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