KWCChangepoint (version 0.2.3)

dwbs: Find changepoints using depth-based wild binary segmentation

Description

Detect multiple changepoints in multivariate data using the depth-based wild binary segmentation algorithm (Ramsay and Chenouri, 2023).

Usage

dwbs(
  data,
  numInt = 10,
  thresh = 1.3584,
  alpha = 1,
  depth = c("spat", "hs", "mahal", "mahal75")
)

Value

A list consisting of:

• $changepoints : Indicies of the change-points detected; will return integer(0) if no change-points are detected.

• $method : A string "DWBS"

Arguments

data

Data in matrix or data.frame form, where each row is an observation and each column is a dimension.

numInt

Number of intervals to be generated.

thresh

Numeric scalar; detection threshold. Larger values make detection more conservative.

alpha

Set as 1 by default, applying a standard SIC penalty. Set to a number larger than 1 for a strengthened SIC.

depth

Depth function.

References

Fryzlewicz, Piotr. “Wild Binary Segmentation for Multiple Change-Point Detection.” The Annals of Statistics 42, no. 6 (2014). https://doi.org/10.1214/14-AOS1245.

Killick, R., P. Fearnhead, and I. A. Eckley. “Optimal Detection of Changepoints With a Linear Computational Cost.” Journal of the American Statistical Association 107, no. 500 (2012): 1590–98. https://doi.org/10.1080/01621459.2012.737745.

Ramsay, K., & Chenouri, S. (2023). Robust nonparametric multiple changepoint detection for multivariate variability. Econometrics and Statistics. https://doi.org/10.1016/j.ecosta.2023.09.001

Examples

set.seed(11)
exdata <- rbind(replicate(3,rnorm(200)),
                replicate(3,rnorm(200,10)),
                replicate(3,rnorm(200,0.2)))
dwbs(data = exdata)

# Increasing `numInt` will result in more accurate detection
dwbs(data = exdata, numInt = 100)