cr.rand.max.inner.prod: The value that maximises the random CUSUM statistic across...

In wbsts: Multiple Change-Point Detection for Nonstationary Time Series

Description

The function finds the value which yields the maximum inner product with the input time series (CUSUM) located between 100(1-p)\% and 100p\% of their support across all the wavelet periodogram scales.

Usage

cr.rand.max.inner.prod(XX,Ts,C_i,epp,M = 0,Plot = FALSE,cstar=0.95)

Arguments

XX	The wavelet periodogram.
Ts	The sample size of the series.
C_i	The CUSUM threshold.
epp	A minimum adjustment for the bias present in E^{(i)}_{t,T}.
M	Number of random CUSUM to be generated.
Plot	Plot the threhsold CUSUM statistics across the wavelet scales.
cstar	A scalar in (0.67,1]

Value

1	Candidate change point
2	The maximum CUSUM value
3	The starting point s of the favourable draw
4	The ending point e of the favourable draw

Author(s)

K. Korkas and P. Fryzlewicz

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

 
#cps=seq(from=1000,to=2000,by=200)
#y=sim.pw.arma(N =3000,sd_u = c(1,1.5,1,1.5,1,1.5,1),
#b.slope=rep(0.99,7),b.slope2 = rep(0.,7), mac = rep(0.,7),br.loc = cps)[[2]]
#z=ews.trans(y,scales=c(11,9,8,7,6))
#out=cr.rand.max.inner.prod(z, Ts = length(y),C_i = tau.fun(y), 
#epp = rep(32,5), M = 2000, cstar = 0.75, Plot = 1)
#abline(v=cps,col="red")

wbsts documentation built on July 1, 2020, 5:23 p.m.