aARC: Automatic adversarially robust univariate mean change point...

In changepoints: A Collection of Change-Point Detection Methods

Automatic adversarially robust univariate mean change point detection.

Description

Perform the adversarially robust change point detection method with automatic selection of the contamination proportion epsilon when treating the inliner distributions as Gaussian.

Usage

aARC(y, t_dat, guess_true = 0.05, h, block_num = 1)

Arguments

y	A numeric vector of observations.
t_dat	A numeric vector of observations that is used to select epsilon in the Huber contamination model.
guess_true	A numeric scalar representing a guess of epsilon value.
h	An integer scalar representing block size.
block_num	An integer scalar representing number of blocks used when searching for local maximiser.

Value

An numeric vector of estimated change point locations.

Author(s)

Mengchu Li

References

Li and Yu (2021) <arXiv:2105.10417>.

Examples

#' ### simulate data with contamination
obs_num = 1000
D = 2
noise = 0.1 # proportion of contamination
mu0 = 0
mu1 = 1
sd =1
idmixture = rbinom(obs_num/D, 1, 1-noise)
dat = NULL
for (j in 1:D){
  for (i in 1:(obs_num/(2*D))){
    if (idmixture[i] == 1){
      dat = c(dat,rnorm(1,mu0,sd))
    }
    else{
      dat = c(dat,rnorm(1,mu1/(2*noise),0))
    }
  }
  for (i in (obs_num/(2*D)+1):(obs_num/D)){
    if (idmixture[i] == 1){
      dat = c(dat,rnorm(1,mu1,sd))
    }
    else{
      dat = c(dat,rnorm(1,mu1/(2*noise)-(1-noise)*mu1/noise,0))
    }
  }
}
plot(dat)
### perform aARC
aARC(dat, dat[1:200], h = 120)

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