Predictions.COST.G: new location prediction by Gaussian copula

In COST: Copula-Based Semiparametric Models for Spatio-Temporal Data

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.

COST documentation built on May 2, 2019, 9:33 a.m.