WBS.multi.nonpar: Wild binary segmentation for multivariate nonparametric...

In changepoints: A Collection of Change-Point Detection Methods

Wild binary segmentation for multivariate nonparametric change points detection.

Description

Perform wild binary segmentation for multivariate nonparametric change points detection.

Usage

WBS.multi.nonpar(Y, W, s, e, Alpha, Beta, h, delta, level = 0)

Arguments

Y	A numeric matrix of observations with with horizontal axis being time, and vertical axis being dimension.
W	A copy of the matrix Y, it can be Y itself.
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.
h	A numeric scalar of bandwidth parameter.
delta	A integer scalar of minimum spacing.
level	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.

Author(s)

Oscar Hernan Madrid Padilla & Haotian Xu

References

Padilla, Yu, Wang and Rinaldo (2019) <arxiv:1910.13289>.

See Also

thresholdBS for obtain change points estimation, tuneBSmultinonpar for a tuning version.

Examples

n = 70
v = c(floor(n/3), 2*floor(n/3)) # location of change points
p = 4
Y = matrix(0, p, n) # matrix for data
mu0 = rep(0, p) # mean of the data
mu1 = rep(0, p)
mu1[1:floor(p/2)] = 2
Sigma0 = diag(p) #Covariance matrices of the data
Sigma1 = diag(p)*2
# Generate data
for(t in 1:n){
  if(t < v[1] || t > v[2]){
     Y[,t] = MASS::mvrnorm(n = 1, mu0, Sigma0)
  }
  if(t >= v[1] && t < v[2]){
     Y[,t] = MASS::mvrnorm(n = 1, mu1, Sigma1)
  }
}## close for generate data
M = 10
intervals = WBS.intervals(M = M, lower = 1, upper = ncol(Y)) #Random intervals
K_max = 30
h = 5*(K_max*log(n)/n)^{1/p} # bandwith
temp = WBS.multi.nonpar(Y, Y, 1, ncol(Y), intervals$Alpha, intervals$Beta, h, delta = 10)
result = thresholdBS(temp, median(temp$Dval))

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