tuneBSuninonpar: Wild binary segmentation for univariate nonparametric change...

In changepoints: A Collection of Change-Point Detection Methods

Wild binary segmentation for univariate nonparametric change points detection with tuning parameter selection.

Description

Perform wild binary segmentation with tuning parameter selection based on sample splitting.

Usage

tuneBSuninonpar(BS_object, Y, N)

Arguments

BS_object	A "BS" object produced by BS.uni.nonpar or WBS.uni.nonpar.
Y	A numeric matrix of observations with horizontal axis being time, and vertical axis being multiple observations on each time point.
N	A integer vector representing number of multiple observations on each time point.

Value

A vector of estimated change points (sorted in strictly increasing order).

Author(s)

Oscar Hernan Madrid Padilla & Haotian Xu

References

Padilla, Yu, Wang and Rinaldo (2021) <doi:10.1214/21-EJS1809>.

See Also

BS.uni.nonpar and WBS.uni.nonpar.

Examples

Y = t(as.matrix(c(rnorm(100, 0, 1), rnorm(100, 0, 10), rnorm(50, 0, 40))))
W = Y # W is a copy of the matrix Y, it can be Y itself.
N = rep(1, 250)
M = 5
set.seed(123)
intervals = WBS.intervals(M = M, lower = 1, upper = ncol(Y))
BS_object = WBS.uni.nonpar(W, 1, ncol(Y), intervals$Alpha, intervals$Beta, N, delta = 5)
cpt_hat = tuneBSuninonpar(BS_object, Y, N)

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