modeHuntingBlock(X.raw, lower = -Inf, upper = Inf, d0 = 2,
m0 = 10, fm = 2, crit.vals, min.int = FALSE)min.int = TRUE, the set of minimal intervals is output, otherwise all intervals with a test
statistic above the critical value (in their respective block) are given.blocks for details how $\mathcal{I}_{app}$ is generated and modeHunting for
a proper introduction to the notation used here.
The function modeHuntingBlock uses the test statistic $T^+_n({\bf X}, \mathcal{B}_r)$,
where $\mathcal{B}_r$ contains all intervals of Block $r$, $r=1,\ldots,\#blocks$.
Critical values for each block individually are received via finding an $\tilde \alpha$ such that
$$P(B_n({\bf{X}}) > q_{r,\tilde \alpha / (r+tail)^\gamma} \ for \ at \ least \ one \ r) \le \alpha,$$
where $q_{r,\alpha}$ is the $(1-\alpha)$--quantile of the distribution of $T^+_n({\bf X}, \mathcal{B}_r).$
We then define the sets $\mathcal{D}^\pm(\alpha)$ as
$$\mathcal{D}^\pm(\alpha) := \Bigl{\mathcal{I}_{jk} \ : \ \pm T_{jk}({\bf{X}}) > q_{r,\tilde \alpha / (r+tail)^\gamma} \, , \ r = 1,\ldots \#blocks\Bigr}.$$
Note that $\gamma$ and $tail$ are automatically determined by $crit.vals$.
If min.int = TRUE, the set $\mathcal{D}^\pm(\alpha)$ is replaced by the set ${\bf{D}}^\pm(\alpha)$
of its minimal elements. An interval $J \in \mathcal{D}^\pm(\alpha)$ is called minimal if
$\mathcal{D}^\pm(\alpha)$ contains no proper subset of $J$. This minimization post-processing
step typically massively reduces the number of intervals. If we are mainly interested in locating the ranges
of increases and decreases of $f$ as precisely as possible, the intervals in
$\mathcal{D}^\pm(\alpha) \setminus \bf{D}^\pm(\alpha)$ do not contain relevant information.modeHunting, modeHuntingApprox, and cvModeBlock.## for examples type
help("mode hunting")
## and check the examples there}
<keyword>htest</keyword>
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