Inspect_calibrate: Generates empirical detection threshold xi using Monte Carlo...

In HDCD: High-Dimensional Changepoint Detection

Generates empirical detection threshold \xi using Monte Carlo simulation

Description

R wrapper for C function choosing empirical detection threshold \xi for the Narrowest-Over-Threshold variant of Inspect \insertCite@as specified in section 4.2 in @moen2023efficientHDCD using Monte Carlo simulation.

Usage

Inspect_calibrate(
  n,
  p,
  N = 100,
  tol = 1/100,
  lambda = NULL,
  alpha = 1.5,
  K = 5,
  eps = 1e-10,
  maxiter = 10000,
  rescale_variance = TRUE,
  debug = FALSE
)

Arguments

n	Number of observations
p	Number time series
N	Number of Monte Carlo samples used
tol	False positive probability tolerance
lambda	Manually specified value of \lambda (can be NULL, in which case \lambda \gets \sqrt{\log(p\log n)/2})
alpha	Parameter for generating seeded intervals
K	Parameter for generating seeded intervals
eps	Threshold for declaring numerical convergence of the power method
maxiter	Maximum number of iterations for the power method
rescale_variance	If TRUE, each row of the data is re-scaled by a MAD estimate using rescale_variance
debug	If TRUE, diagnostic prints are provided during execution

Value

A list containing

max_value

the empirical threshold

References

\insertAllCited

Examples

library(HDCD)
n = 50
p = 50

set.seed(100)
thresholds_emp = Inspect_calibrate(n,p, N=100, tol=1/100)
thresholds_emp$max_value # xi

# Generating data
X = matrix(rnorm(n*p), ncol = n, nrow=p)
# Adding a single sparse change-point:
X[1:5, 26:n] = X[1:5, 26:n] +2

res = Inspect(X, xi = thresholds_emp$max_value)
res$changepoints

HDCD documentation built on June 22, 2024, 10:53 a.m.