get.thres.ar: Selection of thresholds by fitting an AR(p) model

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

Description

The function returns data-driven thresholds and it is described in Korkas and Fryzlewicz (2015) where it is referred as Bsp1. See also the supplementary material for this work.

Usage

get.thres.ar(y, q=.95, r=100, scales=NULL)

Arguments

y	The time series.
q	The quantile of the r simulations.
r	Number of simulations.
scales	The wavelet periodogram scales to be used. If NULL (DEFAULT) then this is selected as described in the main text.

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)

K. Korkas and P. Fryzlewicz (2017), Supplementary material: Multiple change-point detection for non-stationary time series using Wild Binary Segmentation.

Examples

#cps=seq(from=100,to=1200,by=350)
#y=sim.pw.arma(N =1200,sd_u = c(1,1.5,1,1.5,1),
#b.slope=rep(0.99,5),b.slope2 = rep(0.,5), mac = rep(0.,5),br.loc = cps)[[2]]
#C_i=get.thres.ar(y=y, q=.95, r=100, scales=NULL)
#wbs.lsw(y,M=1, C_i = C_i)$cp.aft

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