ccor_boot

Inputs raw data representing two curves, and computes a bootstrap confidence interval
for the curve correlation between them.

Implements a basis function or functional data analysis framework
for several techniques of multivariate analysis in continuous-time
setting. Specifically, we introduced continuous-time analogues of
several classical techniques of multivariate analysis, such as
principal component analysis, canonical correlation analysis,
Fisher linear discriminant analysis, K-means clustering, and so
on. Details are in Biplab Paul, Philip T. Reiss, Erjia Cui and Noemi Foa (2025)
"Continuous-time multivariate analysis" <doi: 10.1080/10618600.2024.2374570>.

Biplab Paul

ctmva

Continuous-Time Multivariate Analysis

Philip Tzvi Reiss

Noemi Foa

Dror Arbiv

ccor_boot function

<dl><dt>y, time, curve, k, min.overlap, min.n</dt>
<dd>see <code>ccor</code></dd>
<dt>ndraw</dt>
<dd>number of bootstrap samples</dd>
<dt>conf</dt>
<dd>confidence level</dd></dl>

Arguments

Philip Tzvi Reiss &lt;reiss@stat.haifa.ac.il&gt;, Noemi Foa, Dror Arbiv and Biplab Paul &lt;paul.biplab497@gmail.com&gt;

Author

Bootstrap confidence interval estimation for curve correlation — ccor_boot

<dl>

<dt>y, time, curve, k, min.overlap, min.n</dt>
<dd>see <code>ccor</code></dd>


<dt>ndraw</dt>
<dd>number of bootstrap samples</dd>


<dt>conf</dt>
<dd>confidence level</dd>

</dl>

ccor_boot: Bootstrap confidence interval estimation for curve correlation

Description

Usage

Value

Arguments

Author

See Also

Examples