locate.change.missing: Single changepoint estimation with missing data

In InspectChangepoint: High-Dimensional Changepoint Estimation via Sparse Projection

Single changepoint estimation with missing data

Description

Single changepoint estimation with missing data

Usage

locate.change.missing(
  x,
  lambda,
  standardize.series = FALSE,
  view.cusum = FALSE
)

Arguments

x	A (p x n) data matrix of multivariate time series, each column represents a data point
lambda	Regularisation parameter. If no value is supplied, the dafault value is chosen to be sqrt(log(log(n)*p/2)) for p and n number of rows and columns of the data matrix x respectively.
standardize.series	Whether the given time series should be standardised before estimating the projection direction. Default is FALSE, i.e. the input series is assume to have variance 1 in each coordinate.
view.cusum	Whether to show a plot of the projected CUSUM series

Value

A list of two items:

• changepoint - A single integer value estimate of the changepoint location is returned. If the estimated changepoint is z, it means that the multivariate time series is piecewise constant up to z and from z+1 onwards.

• cusum - The maximum absolute CUSUM statistic of the projected univariate time series associated with the estimated changepoint.

• vector.proj - the vector of projection, which is proportional to an estimate of the vector of change.

References

Wang, T., Samworth, R. J. (2016) High-dimensional changepoint estimation via sparse projection. Arxiv preprint: arxiv1606.06246.

Examples

n <- 2000; p <- 1000; k <- 32; z <- 400; vartheta <- 0.12; sigma <- 1; shape <- 3
noise <- 0; corr <- 0
obj <- single.change(n,p,k,z,vartheta,sigma,shape,noise,corr)
x <- obj$x
locate.change(x)

InspectChangepoint documentation built on May 3, 2022, 9:07 a.m.