Multivariate Spatial Modeling for Geostatistical Data Using Convolved Covariance Functions
By Anandamayee Majumdar, Alan E. Gelfand. 2007
113 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Environmental Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Marc G. Genton
- Martin Schlather
- Emilio Porcu
- Moreno Bevilacqua
- Tatiyana V. Apanasovich
- Ying Sun
- Tilmann Gneiting
- William Kleiber
In Environmental Engineering
- Mikyoung Jun
- Emilio Porcu
- Marc G. Genton
- Moreno Bevilacqua
- Martin Schlather
- Tatiyana V. Apanasovich
- Tilmann Gneiting
- Ying Sun
Citations in the corpus
- Classes of compactly supported covariance functions for multivariate random fields (2015, background): “Recent literature (Gneiting et al. 2010; Porcu and Zastavnyi 2011; Apanasovich and Genton 2010; Apanasovich et al. 2012; Majumdar and Gelfand 2007; Kleiber and Porcu 2014; Hristopoulos and Porcu 2014; Alonso-Malaver et al. 2013; Ruiz-Medina and Porcu 2014; Alonso-Malaver et al. 2013) as well as earl”
- Non‐stationary Cross‐Covariance Models for Multivariate Processes on a Globe (2011, background): “Majumdar & Gelfand (2007) present an approach to modelling stationary processes based on convolving covariance functions.”
- Matérn Cross-Covariance Functions for Multivariate Random Fields (2010, background): “The crux of the argument is contained in the following statement, which is a variant of a result of Gaspari et al. (2006, p. 1820) and theorem 1 of Majumdar and Gelfand (2007), and is also proved in the Appendix.”
- Some topics in convolution-based spatial modeling (2007, methodology): “Other convolution-based multivariate spatial models include the multivariate space-time models in Calder (2007a,b) and the convolutions of covariance functios developed in Majumdar and Gelfand (2007).”
- A Valid Matérn Class of Cross-Covariance Functions for Multivariate Random Fields With Any Number of Components (2012, methodology): “Other approaches to valid cross-covariance models include kernel convolution (Ver Hoef and Barry 1998) and covariance convolution (Gaspari and Cohn 1999; Majumdar and Gelfand 2007) methods, both of which generally require numerical integration.”