WBS.uni.rob: Robust wild binary segmentation for univariate mean change...

In changepoints: A Collection of Change-Point Detection Methods

Robust wild binary segmentation for univariate mean change points detection.

Description

Perform a robust version of the wild binary segmentation method using Huber loss.

Usage

WBS.uni.rob(y, s, e, Alpha, Beta, K = 1.345, delta, level = 0)

Arguments

y	A numeric vector of observations.
s	A numeric scalar representing the starting index of an interval.
e	A numeric scalar representing the ending index of an interval.
Alpha	An integer vector of starting indices of random intervals.
Beta	An integer vector of ending indices of random intervals.
K	A numeric scalar representing the robustification parameter in the Huber loss.
delta	A positive integer scalar of minimum spacing.
level	Should be fixed as 0.

Value

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

S	A vector of estimated change points (sorted in strictly increasing order).
Dval	A vector of values of CUSUM statistic based on KS distance.
Level	A vector representing the levels at which each change point is detected.
Parent	A matrix with the starting indices on the first row and the ending indices on the second row.

Author(s)

Mengchu Li & Haotian Xu

References

Fearnhead & Rigaill (2019) <doi:10.1080/01621459.2017.1385466>.

See Also

thresholdBS for obtaining change points estimation.

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