BS.cov: Binary Segmentation for covariance change points detection...

In changepoints: A Collection of Change-Point Detection Methods

Binary Segmentation for covariance change points detection through Operator Norm.

Description

Perform binary segmentation for covariance change points detection through Operator Norm.

Usage

BS.cov(X, s, e, level = 0)

Arguments

X	A numeric matrix of observations with with horizontal axis being time, and vertical axis being dimensions.
s	A integer scalar of starting index.
e	A integer scalar of ending index.
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 structure:

• S: A vector of estimated changepoints (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.

Author(s)

Haotian Xu

References

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

See Also

thresholdBS for obtain change points estimation.

Examples

p = 10
A1 = gen.cov.mat(p, 1, "equal")
A2 = gen.cov.mat(p, 2, "diagonal")
A3 = gen.cov.mat(p, 3, "power")
X = cbind(t(MASS::mvrnorm(100, mu = rep(0, p), A1)), 
          t(MASS::mvrnorm(150, mu = rep(0, p), A2)), 
          t(MASS::mvrnorm(200, mu = rep(0, p), A3)))
temp = BS.cov(X, 1, 450)
thresholdBS(temp, 10)

changepoints documentation built on Sept. 4, 2022, 5:06 p.m.