modehunt (version 1.0.7)

cvModeBlock: Critical values for test statistic based on the block procedure

Description

This dataset contains critical values for some $n$ and $\alpha$ for the block procedure.

Usage

data(cvModeBlock)

format

A data frame providing 15 different combinations of $n$ and $\alpha$ and the following columns: ll{ alpha The levels at which critical values were simulated. n The number of observations for which critical values were simulated. block 1 - 9 Critical values for the respective blocks. }

Remember

$n$ is the number of interior observations, i.e. if you are analyzing a sample of size $m$, then you need critical values corresponding to ll{ n = m-2 If no additional information on $a$ and $b$ is available. n = m-1 If either $a$ or $b$ is known to be a certain finite number. n = m If both $a$ and $b$ are known to be certain finite numbers, } where $[a,b] = {x \ : \ f(x) > 0}$ is the support of $f$.

source

These critical values were generated using the function criticalValuesBlock. Critical values for other combinations for $\alpha$ and $n$ can be computed using this latter function.

Details

For details see modeHunting. Critical values are based on $M=100'000$ simulations of i.i.d. random vectors $${\bf{U}} = (U_1,\dots,U_n)$$ where $U_i$ is a uniformly on $[0,1]$ distributed random variable, $i=1,\dots,M$.

References

Rufibach, K. and Walther, G. (2010). A general criterion for multiscale inference. J. Comput. Graph. Statist., 19, 175--190.

Examples

## extract critical values for alpha = 0.05, n = 200
data(cvModeBlock)
cv <- cvModeBlock[cvModeBlock$alpha == 0.05 & cvModeBlock$n == 200, 3:11]
cv