multilag.cpts.merge {CptNonPar}

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 list object, where each element of the list is the output of the np.mojo function computed at a different lag.

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

• "sequential": starting from the left-most change point estimator and proceeding forward in time, estimators are grouped into clusters based on mutual distance. The estimator yielding the smallest corresponding p-value is chosen as the change point estimator for that cluster. See McGonigle and Cho (2023) for details.

• "bottom-up": starting with the smallest p-value, the change points are merged using bottom-up merging (Messer et al. (2014)).

Details

See McGonigle and Cho (2023) 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 p-value given in columns.

cpt.clusters

A list object of length given by the number of detected change points. Each field contains a matrix of all change point estimators that are declared to be associated to the corresponding change point in the cpts field.

References

McGonigle, E.T., Cho, H. (2023). Nonparametric data segmentation in multivariate time series via joint characteristic functions. arXiv preprint arXiv:2305.07581.

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

np.mojo, np.mojo.multilag

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