GPFDA (version 1.1)

GPFDA-package: Gaussian Process in Functional Data Analysis

Description

uses functional regression to be the mean function, and the Gaussian Process to be the covariance structure.

$$y_m(t)=mu_m(t)+tau_m(x)+epsilon_m(t)$$

Where $m$ is the $m^{th}$ data or curve; $\mu_m$ is from functional regression; and $\tau_m$ is from Gaussian Process regression with mean 0 covariance matrix $k({\bf\theta})$.

Details

ll{ Package: GPFDA Type: Package Version: 1.0 Date: 2013-09-30 License: }

References

Shi, J Q., and Choi, T. (2011), Gaussian Process Regression Analysis for Functional Data, Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed., Springer, New York.