DP.univar: Dynamic programming for univariate mean change points...

In changepoints: A Collection of Change-Point Detection Methods

Dynamic programming for univariate mean change points detection through l_0 penalty.

Description

Perform dynamic programming for univariate mean change points detection.

Usage

DP.univar(y, gamma, delta)

Arguments

y	A numeric vector of observations.
gamma	A numeric scalar of the tuning parameter associated with l_0 penalty.
delta	A positive integer scalar of minimum spacing.

Value

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

partition	A vector of the best partition.
yhat	A vector of mean estimation for corresponding to the best partition.
cpt	A vector of change points estimation.

Author(s)

Haotian Xu

References

Wang, Yu and Rinaldo (2020) <doi:10.1214/20-EJS1710>

Examples

set.seed(123)
cpt_true = c(20, 50, 170)
y = rnorm(300) + c(rep(0,20),rep(1,30),rep(0,120),rep(1,130))
DP_result = DP.univar(y, gamma = 5, delta = 5)
cpt_hat = DP_result$cpt
Hausdorff.dist(cpt_hat, cpt_true)

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