wise_sim

Returns an n by n similarity matrix.

A fast implementation of the weighted information similarity aggregation (WISE)
test for detecting serial dependence, particularly suited for high-dimensional and
non-Euclidean time series. Includes functions for constructing similarity matrices
and conducting hypothesis testing. Users can use different similarity
measures and define their own weighting schemes. For more details see Q Zhu, M Liu,
Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.

Qihua Zhu

SimIndep

WISE: a Weighted Similarity Aggregation Test for Serial
Independence

Mingshuo Liu

Yuefeng Han

Doudou Zhou

wise_sim function

<dl><dt>data</dt>
<dd>an n by p data matrix, with n being the sample size and p being the dimension.</dd>
<dt>measure</dt>
<dd>the similarity measure: "distance" for distance-based measure; "graph" for k-nearest neighbor graph-based measure. The default is "distance".</dd>
<dt>metric</dt>
<dd>character string specifying the distance metric or graph weight. "manhattan" for Manhattan distance (default), "euclidean" for Euclidean distance.</dd>
<dt>k</dt>
<dd>the Number of nearest neighbors used in k-nearest neighbor graph. k = floor(sqrt(n)) if not specified.</dd></dl>

Arguments

Calculate an n by n similarity matrix — wise_sim

<dl>

<dt>data</dt>
<dd>an n by p data matrix, with n being the sample size and p being the dimension.</dd>


<dt>measure</dt>
<dd>the similarity measure: "distance" for distance-based measure; "graph" for k-nearest neighbor graph-based measure. The default is "distance".</dd>


<dt>metric</dt>
<dd>character string specifying the distance metric or graph weight. "manhattan" for Manhattan distance (default), "euclidean" for Euclidean distance.</dd>


<dt>k</dt>
<dd>the Number of nearest neighbors used in k-nearest neighbor graph. k = floor(sqrt(n)) if not specified.</dd>

</dl>

wise_sim: Calculate an n by n similarity matrix

Description

Usage

Value

Arguments

Examples