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 \(k({\bf\theta})\).
| Package: | GPFDA |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2013-09-30 |
| License: |
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.