boot_thresh {eNchange} | R Documentation |
A bootstrap method to calculate the threshold (stopping rule) in the BS or EBS segmentation described in Cho and Korkas (2018) and adapted for irregularly time series in Korkas (2020).
boot_thresh(
H,
q = 0.75,
r = 100,
p = 1,
start.values = c(0.9, 0.6),
process = "acd",
do.parallel = 2,
dampen.factor = "auto",
epsilon = 1e-05,
LOG = TRUE,
acd_p = 0,
acd_q = 1
)
## S4 method for signature 'ANY'
boot_thresh(
H,
q = 0.75,
r = 100,
p = 1,
start.values = c(0.9, 0.6),
process = "acd",
do.parallel = 2,
dampen.factor = "auto",
epsilon = 1e-05,
LOG = TRUE,
acd_p = 0,
acd_q = 1
)
H |
The input irregular time series. |
q |
The bootstrap distribution quantile. Default is 0.75. |
r |
The number of bootrstap simulations. Default is 100. |
p |
The support of the CUSUM statistic. Default is 1. |
start.values |
Warm starts for the optimizers of the likelihood functions. |
process |
Choose between acd or hawkes. Default is acd. |
do.parallel |
Choose the number of cores for parallel computation. If 0 no parallelism is done. Default is 2. |
dampen.factor |
The dampen factor in the denominator of the residual process. Default is "auto". |
epsilon |
A parameter added to ensure the boundness of the residual process. Default is 1e-5. |
LOG |
Take the log of the residual process. Default is TRUE. |
acd_p |
The p order of the ACD model. Default is 0. |
acd_q |
The q order of the ACD model. Default is 1. |
Returns the threshold C
.
Cho, Haeran, and Karolos Korkas. "High-dimensional GARCH process segmentation with an application to Value-at-Risk." arXiv preprint <arXiv:1706.01155> (2018).
pw.acd.obj <- new("simACD")
pw.acd.obj@cp.loc <- c(0.25,0.75)
pw.acd.obj@lambda_0 <- c(1,2,1)
pw.acd.obj@alpha <- rep(0.2,3)
pw.acd.obj@beta <- rep(0.7,3)
pw.acd.obj@N <- 3000
pw.acd.obj <- pc_acdsim(pw.acd.obj)
boot_thresh(pw.acd.obj@x,r=20)