CV.search.DP.univar: Grid search for dynamic programming to select the tuning...

In changepoints: A Collection of Change-Point Detection Methods

Grid search for dynamic programming to select the tuning parameter through Cross-Validation.

Description

Perform grid search for dynamic programming to select the tuning parameter through Cross-Validation.

Usage

CV.search.DP.univar(y, gamma_set, delta)

Arguments

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

Value

A list with the following structure:

cpt_hat	A list of vector of estimated change points (sorted in strictly increasing order).
K_hat	A list of scalar of number of estimated change points.
test_error	A list of vector of testing errors.
train_error	A list of vector of training errors.

Author(s)

Daren Wang & Haotian Xu

References

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

Examples

set.seed(0)
cpt_true = c(20, 50, 170)
y = rnorm(300) + c(rep(0,20),rep(2,30),rep(0,120),rep(2,130))
gamma_set = 1:5
DP_result = CV.search.DP.univar(y, gamma_set, delta = 5)
min_idx = which.min(DP_result$test_error)
cpt_hat = unlist(DP_result$cpt_hat[min_idx])
Hausdorff.dist(cpt_hat, cpt_true)

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