WBS.uni.rob {changepoints}

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.