BEMA: BEMA for the standard spiked covariance model

In CHuanSite/PJD: Partial and Joint Decomposiiton(PJD)

Description

Apply BEMA algorithm for spiked covariance proposed in the paper "Estimation of the number of spiked eigenvalues in a covariance matrix by bulk eigenvalue matching analysis""

Usage

BEMA(eigenvalue, p, n, alpha = 0.2, beta = 0.1)

Arguments

eigenvalue	a list of eigenvalues to choose from
p	dimension of the features
n	number of samples
alpha	a tuning parameter in the analysis, a default value is set to 0.2
beta	a tuning parameter on computing quantile in Tracy-Widom, a default value is set to be 0.1

Value

The total number of spikes extracted, K

Examples

x = matrix(rnorm(1000, 100), nrow = 1000)
eigen_x = svd(x)
eigen_out = list(eigenvalue = eigen_x$d^2 / 100, p = 1000, n = 100)
BEMA(eigen_out$eigenvalue, p = 1000, n = 100)

CHuanSite/PJD documentation built on Oct. 26, 2021, 1 p.m.