Maximum likelihood estimation of the dynamic coregionalization model with heterotopic data

77 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Generalized Spatial and Spatiotemporal Autoregressive Conditional Heteroscedasticity (2016, background): “…a wide range of applications can be found in empirical research, including econometrics (e.g., Holly et al. 2010, Fingleton 2008b), biometrics (e.g., Shinkareva et al. 2006, Ho et al. 2005, MacNab & Dean 2001) or environmetrics (e.g., Fassò & Finazzi 2011, Fassò et al. 2007, Fuentes 2001).”
Coupled stochastic weather generation using spatial and generalized linear models (2015, background): “This transformation approach is O(s, t) = [WO(s,t)≥0] called a spatially varying anamorphosis function (Chilès and Delfiner 1999), which retains the gamma distribution at individual locations but allows for spatial correlation between locations.”
Spatial and spatio-temporal models with R-INLA. (2013, unknown): “meteorological and geographical variables) as well as time and space dependence (e.g. Cocchi et al., 2007; Cameletti et al., 2011a; Sahu, 2012; Fassò and Finazzi, 2011).”
Spatial and spatio-temporal models with R-INLA. (2013, background): “meteorological and geographical variables) as well as time and space dependence (e.g. Cocchi et al., 2007; Cameletti et al., 2011a; Sahu, 2012; Fassò and Finazzi, 2011).”