This function computes the WSCE estimator for large covariances in the presence of pairwise and spatial covariates from Metodiev et al. (2024).
wsce(
pairwise_covariate_matrices,
adj_matrix,
dataset,
mean_estim = NULL,
sd_estim = NULL,
grid_size = 100,
parallelize = FALSE,
ncores = 8,
adj_positions = 1:nrow(adj_matrix),
interaction_effects = list(),
init = NULL,
sce_init = NULL,
use_bootstrap = FALSE,
num_bootstrap_iters = 100,
seed = 0,
verbose = TRUE
)Returns a named list with the following elements:
parm, estimated parameters of pairwise, spatial effects, average_effects, average effects of the covariates, corrmat_estim, estimator of the correlation matrix, covmat_estim, estimator of the covariance matrix, bic, the Bayesian information criterion (BIC), lambda, the asymptotically optimal weight of the WSCE
named list of square matrices
adjacency matrix of the spatial covariate
the dataset given in matrix form
mean vector estimate
standard deviation vector estimate
grid-size for spatial effect
uses parallel-processing if TRUE
number of cores for the parallelization
positions within the adjacency matrix
list of interaction effects
the initialization parameter vector
the sce-initialization parameter vector
uses bootstrapping if TRUE
number of bootstrap simulations
a seed
prints progress if TRUE
Metodiev, M., Perrot-Dockès, M., Ouadah, S., Fosdick, B. K., Robin, S., Latouche, P., & Raftery, A. E. (2024). A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates. arXiv preprint arXiv:2411.04520.