online.univar: Online change point detection with controlled false alarm...

In changepoints: A Collection of Change-Point Detection Methods

Online change point detection with controlled false alarm rate or average run length.

Description

Perform online change point detection with controlled false alarm rate or average run length.

Usage

online.univar(
  y_vec,
  b_vec = NULL,
  train_vec = NULL,
  alpha = NULL,
  gamma = NULL,
  permu_num = NULL
)

Arguments

y_vec	A numeric vector of observations.
b_vec	A numeric vector of thresholds b_t with t >= 2.
train_vec	A numeric vector of training data from a pre-change distribution (no change point), which is only needed to when b_vec is NULL in order to calibrate b_t.
alpha	A numeric scalar of desired false alarm rate.
gamma	An integer scalar of desired average run length.
permu_num	An integer scalar of number of random permutation for calibration.

Value

A list with the following structure:

cpt_hat	An integer scalar of estimated change point location
b_vec	A numeric vector of thresholds b_t with t >= 2

Author(s)

Haotian Xu

References

Yu, Padilla, Wang and Rinaldo (2020) <arxiv:2006.03283>

Examples

y_vec = rnorm(150) + c(rep(0, 100), rep(1, 50))
train_vec = rnorm(100)
# control the false alarm rate
temp1 = online.univar(y_vec = y_vec, train_vec = train_vec, alpha = 0.05, permu_num = 20)
temp1$cpt_hat
temp1$b_vec # calibrated threshold

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