model.lp {breakfast}

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* the value suggested in Fryzlewicz (2020)) is used, see th.const in model.sdll

Details

Further information can be found in Cho and Kirch (2022).

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 (2022) Two-stage data segmentation permitting multiscale change points, heavy tails and dependence. Annals of the Institute of Statistical Mathematics, 74(4), 653–684.

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))