| Predictions.COST.G {COST} | R Documentation |
new location prediction by Gaussian copula
Description
new location prediction by Gaussian copula, where the copula dimension is extended, and the marginal CDF of the new location is estimated by neighboring information; it gives 0.025-, 0.975- and 0.5-th conditional quantiles of the conditional distribution for each new location, at time n, conditional on observed locations at time n-1 and n; both point and interval predictions are provided
Usage
Predictions.COST.G(par,Y,s.ob,s.new,isotropic)
Arguments
par |
parameters in the copula function |
Y |
observed data |
s.ob |
coordinates of observed locations |
s.new |
coordinates of new locations |
isotropic |
indicator, True for isotropic correlation matrix, False for anisotropic correlation matrix, and we usually choose False for flexibility |
Value
0.025-, 0.975- and 0.5-th conditional quantiles of the conditional distribution for each new location, at time n
Author(s)
Yanlin Tang and Huixia Judy Wang
References
Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.