multilag.cpts.merge {CptNonPar} | R Documentation |
Merge Change Point Estimators from Multiple Lags
Description
Merges change point estimators from different lagged values into a final set of overall change point estimators.
Usage
multilag.cpts.merge(
x.c,
eta.merge = 1,
merge.type = c("sequential", "bottom-up")[1]
)
Arguments
x.c |
A |
eta.merge |
A positive numeric value for the minimal mutual distance of changes, relative to bandwidth, used to merge change point estimators across different lags. |
merge.type |
String indicating the method used to merge change point estimators from different lags. Possible choices are
|
Details
See McGonigle and Cho (2025) for further details.
Value
A list
object which contains the following fields
cpts |
A matrix with rows corresponding to final change point estimators, with estimated change point location and associated lag and importance score given in columns. |
cpt.clusters |
A |
References
McGonigle, E.T., Cho, H. (2025). Nonparametric data segmentation in multivariate time series via joint characteristic functions. Biometrika (to appear).
Messer M., Kirchner M., Schiemann J., Roeper J., Neininger R., Schneider G. (2014). A Multiple Filter Test for the Detection of Rate Changes in Renewal Processes with Varying Variance. The Annals of Applied Statistics, 8(4), 2027-2067.
See Also
Examples
set.seed(1)
n <- 500
noise <- c(rep(1, 300), rep(0.4, 200)) * stats::arima.sim(model = list(ar = 0.3), n = n)
signal <- c(rep(0, 100), rep(2, 400))
x <- signal + noise
x.c0 <- np.mojo(x, G = 83, lag = 0)
x.c1 <- np.mojo(x, G = 83, lag = 1)
x.c <- multilag.cpts.merge(list(x.c0, x.c1))
x.c