| get.thres.ar {wbsts} | R Documentation |
Selection of thresholds by fitting an AR(p) model
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