WBSIP.cov {changepoints} R Documentation

## Wild binary segmentation for covariance change points detection through Independent Projection.

### Description

Perform wild binary segmentation for covariance change points detection through Independent Projection

### Usage

WBSIP.cov(X, X_prime, s, e, Alpha, Beta, delta, level = 0)


### Arguments

 X A numeric vector of observations. X_prime A numeric vector of observations which are independent copy of X. s A integer scalar of starting index. e A integer scalar of ending index. Alpha A integer vector of starting indices of random intervals. Beta A integer vector of ending indices of random intervals. delta A positive integer scalar of minimum spacing. level A parameter for tracking the level at which a change point is detected. Should be fixed as 0.

### Value

An object of class "BS", which is a list with the following structure:

 S A vector of estimated change points (sorted in strictly increasing order) Dval A vector of values of CUSUM statistic based on KS distance Level A vector representing the levels at which each change point is detected Parent A matrix with the starting indices on the first row and the ending indices on the second row

Haotian Xu

### References

Wang, Yu and Rinaldo (2021) <doi:10.3150/20-BEJ1249>.

thresholdBS for obtain change points estimation.

### Examples

p = 10
A1 = gen.cov.mat(p, 1, "equal")
A2 = gen.cov.mat(p, 3, "power")
A3 = A1
set.seed(1234)
X = cbind(t(MASS::mvrnorm(50, mu = rep(0, p), A1)),
t(MASS::mvrnorm(50, mu = rep(0, p), A2)),
t(MASS::mvrnorm(50, mu = rep(0, p), A3)))
X_prime = cbind(t(MASS::mvrnorm(50, mu = rep(0, p), A1)),
t(MASS::mvrnorm(50, mu = rep(0, p), A2)),
t(MASS::mvrnorm(50, mu = rep(0, p), A3)))
intervals = WBS.intervals(M = 120, lower = 1, upper = dim(X)[2])
temp = WBSIP.cov(X, X_prime, 1, dim(X)[2], intervals$Alpha, intervals$Beta, delta = 5)
tau = sqrt(p*log(ncol(X)))*1.5
sort(thresholdBS(temp, tau)\$cpt_hat[,1])


[Package changepoints version 1.1.0 Index]