model.lp: Estimating change-points in the piecewise-constant mean of a...

In breakfast: Methods for Fast Multiple Change-Point Detection and Estimation

Estimating change-points in the piecewise-constant mean of a noisy data sequence via the localised pruning

Description

This function estimates the number and locations of change-points in the piecewise-constant mean of a noisy data sequence via the localised pruning method, which performs a Schwarz criterion-based model selection on the given candidate set in a localised way.

Usage

model.lp(
  cptpath.object,
  min.d = 5,
  penalty = c("log", "polynomial"),
  pen.exp = 1.01,
  do.thr = TRUE,
  th.const = 0.5
)

Arguments

cptpath.object	A solution-path object, returned by a sol.[name] routine. Note that the field cptpath.object$x contains the input data sequence.
min.d	A number specifying the minimal spacing between change points; min.d = 5 by default
penalty	A string specifying the type of penalty term to be used in Schwarz criterion; possible values are: "log" Use penalty = log(length(x))^pen.exp "polynomial" Use penalty = length(x)^pen.exp
pen.exp	Exponent for the penalty term (see penalty)
do.thr	If do.thr = TRUE, mild threshoding on the CUSUM test statistics is performed after internal standardisation step in order to "pre-prune down" the candidates
th.const	A constant multiplied to sqrt(2*log(length(x))) to form a mild threshold; if not supplied, a default value (0.5* a value suggested in Fryzlewicz (2020) is used, see th.const in model.sdll

Details

Further information can be found in Cho and Kirch (2021).

Value

An S3 object of class cptmodel, which contains the following fields:

solution.path	The solution path method used to obtain cptpath.object
model.selection	The model selection method used to return the final change-point estimators object, here its value is "lp"
no.of.cpt	The number of estimated change-points in the piecewise-constant mean of the vector cptpath.object$x
cpts	The locations of estimated change-points in the piecewise-constant mean of the vector cptpath.object$x. These are the end-points of the corresponding constant-mean intervals
est	An estimate of the piecewise-constant mean of the vector cptpath.object$x; the values are the sample means of the data (replicated a suitable number of times) between each pair of consecutive detected change-points

References

H. Cho & C. Kirch (2021) Two-stage data segmentation permitting multiscale change points, heavy tails and dependence. arXiv preprint arXiv:1910.12486.

See Also

sol.idetect, sol.idetect_seq, sol.not, sol.tguh, sol.wbs, sol.wbs2, breakfast

Examples

f <- rep(rep(c(0, 1), each = 50), 10)
x <- f + rnorm(length(f)) * .5
model.lp(sol.not(x))

breakfast documentation built on Oct. 18, 2022, 5:08 p.m.