Description Usage Arguments Value References Examples
This is a linear algorithm for quantile simulation under null hypothesis in multiscale change-point segmentation.
1 2 | fastQuantile(alpha, n, r=round(50/min(alpha, 1-alpha)),
mType=c("norm-pen","pois"), seed = 123, ...)
|
alpha |
a scalar with values in [0, 1]; the |
n |
number of observations |
r |
number of Monte Carlo simulations |
mType |
"norm-pen" simulates the multiscale statistic from Normal regression model, "pois" simulates the multiscale statistic from Poission regression model. |
seed |
data seed |
... |
further arguments passed to penalty function |
A scalar quantile value q.
Frick, K., Munk, A., and Sieling, H. (2014). Multiscale Change-Point Inference. J. R. Statist. Soc. B, with discussion and rejoinder by the authors, 76:495–580.
Li, H., Munk, A., and Sieling, H. (2015). FDR-control in multiscale change-point segmentation. arXiv:1412.5844.
1 2 3 | # simulate quantiles for multiscale statistics from Normal regression model
seed = 123
q <- fastQuantile(0.9, 500, 100, mType = "norm-pen")
|
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