uh.wbs {wbsts}

The Wild Binary Segmentation algorithm

Description

The function implements the Wild Binary Segmentation method and aggregates the change-points across the wavelet periodogram. Currently only the Method 2 of aggregation is implemented.

Usage

uh.wbs(z,C_i, del=-1, epp, scale,M=0,cstar=0.75)

Arguments

z

The wavelet periodogram matrix.

C_i

The CUSUM threshold.

del

The minimum allowed size of a segment.

epp

A minimum adjustment for the bias present in E^{(i)}_{t,T}.

scale

Which wavelet periodogram scales to be used.

M

The maximum number of random intervals drawn. If M=0 (DEFAULT) this is selected to be a linear function of the sample size of y. If M=1 then the segmentation is conducted via the Binary segmentation method.

cstar

This refers to the unbalanceness parameter c_{\star}.

Value

cp.bef

Returns the estimated change-points before post-processing

cp.aft

Returns the estimated change-points after post-processing

References

K. Korkas and P. Fryzlewicz (2017), Multiple change-point detection for non-stationary time series using Wild Binary Segmentation. Statistica Sinica, 27, 287-311. (http://stats.lse.ac.uk/fryzlewicz/WBS_LSW/WBS_LSW.pdf)

Examples

#### Generate a highly persistent time series with changing variance and of length 5,000
###Location of the change-points
#cps=seq(from=1000,to=2800,by=200)
#y=sim.pw.arma(N =3000,sd_u = c(1,1.5,1,1.5,1,1.5,1,1.5,1,1.5,1),
#b.slope=rep(0.99,11),b.slope2 = rep(0.,11), mac = rep(0.,11),br.loc = cps)[[2]]
###Estimate the change points via Binary Segmentation
#wbs.lsw(y,M=1)$cp.aft
###Estimate the change points via Wild Binary Segmentation
#wbs.lsw(y,M=0)$cp.aft