linearQ (version 2.0)

fastQuantile: linear Algorithm for Quantile Simulation

Description

This is a linear algorithm for quantile simulation under null hypothesis in multiscale change-point segmentation.

Usage

fastQuantile(alpha, n, r=round(50/min(alpha, 1-alpha)), 
              mType=c("norm-pen","pois"), seed = 123, ...)

Arguments

alpha

a scalar with values in [0, 1]; the alpha-quantile of the null distribution of the multiscale statistic via Monte Carlo simulation

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

Value

A scalar quantile value q.

References

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.

Examples

# NOT RUN {
# simulate quantiles for multiscale statistics from Normal regression model
    seed = 123
    q  <- fastQuantile(0.9, 500, 100, mType = "norm-pen")
# }