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.

data(cvModeBlock)

A data frame providing 15 different combinations of $n$ and $\alpha$ and the following columns:

`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.

$n$ is the number of interior observations, i.e. if you are analyzing a sample of size $m$, then you need critical values corresponding to

`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 $f$.

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, $i=1,\dots,M$.

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

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

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`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,