scd_est: Estimate the Separable Component Decomposition

In TMasak/surfcov: Random Surfaces' Covariance Estimation beyond Separability

Estimate the Separable Component Decomposition

Description

Calculates the R-separable estimator of the covariance of X via the generalized power iteration, starting from B.

Usage

scd_est(X, R = 1, maxiter = 10, B = NULL)

Arguments

X	data set, array of size N x K1 x K2
R	integer, the degree of separability
maxiter	maximum number of iteration
B	a spatial kernel used as a starting point

Value

list of 3: A - array of size R x K1 x K1, the first R temporal kernels; B - array of size R x K2 x K2, the first R spatial kernels; sigma - vector of length R, the separable component scores

Examples

X <- array(runif(20*3*4),c(20,3,4))
scd_est(X,R=2)

TMasak/surfcov documentation built on April 25, 2022, 12:15 a.m.