preProcessX

Preprocesses the initial data vector X.raw according to whether the upper and/or lower endpoint of the support
of f is known.

htest

nonparametric

Given independent and identically distributed observations X(1), ..., X(n) from a density f,
provides five methods to perform a multiscale analysis about f as well as the necessary critical
values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
correction term Gamma in either method offers another two approaches which are more powerful on small scales and
less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
equivalent to the first and was introduced in Rufibach and Walther (2010).

Kaspar Rufibach

modehunt

Multiscale Analysis for Density Functions

Guenther Walther

preProcessX function

<dl> <dt>X.raw</dt>
<dd>Vector of observations.</dd> <dt>lower</dt>
<dd>Lower support point of \(f\), if known.</dd> <dt>upper</dt>
<dd>Upper support point of \(f\), if known.</dd></dl>

Arguments

Kaspar Rufibach, <a href="/link/kaspar.rufibach%40gmail.com?package=modehunt&version=1.0.8" data-mini-rdoc="modehunt::kaspar.rufibach@gmail.com">kaspar.rufibach@gmail.com</a>, <a href="http://www.kasparrufibach.ch">http://www.kasparrufibach.ch</a>
Guenther Walther, <a href="/link/gwalther%40stanford.edu?package=modehunt&version=1.0.8" data-mini-rdoc="modehunt::gwalther@stanford.edu">gwalther@stanford.edu</a>, <a href="https://gwalther.su.domains/">https://gwalther.su.domains/</a>

Author

Prepare data vector according to available information on support endpoints of f — preProcessX

<dl>

 <dt>X.raw</dt>
<dd>Vector of observations.</dd>

 <dt>lower</dt>
<dd>Lower support point of \(f\), if known.</dd>

 <dt>upper</dt>
<dd>Upper support point of \(f\), if known.</dd>

</dl>

Kaspar Rufibach, <a href='mailto:kaspar.rufibach@gmail.com'>kaspar.rufibach@gmail.com</a>, <a href='http://www.kasparrufibach.ch'>http://www.kasparrufibach.ch</a>
Guenther Walther, <a href='mailto:gwalther@stanford.edu'>gwalther@stanford.edu</a>, <a href='https://gwalther.su.domains/'>https://gwalther.su.domains/</a>

preProcessX: Prepare data vector according to available information on support endpoints of f

Description

Usage

Value

Arguments

Author

Details