learningK

This function chooses an optimal parameter k (denoting the number of curves
within the envelope that are considered for reconstruction).

Integrated Functional Depth for Partially Observed Functional Data and applications to visualization, outlier detection and classification. It implements the methods proposed in: Elías, A., Jiménez, R., Paganoni, A. M. and Sangalli, L. M., (2023), "Integrated Depth for Partially Observed Functional Data", Journal of Computational and Graphical Statistics, <doi:10.1080/10618600.2022.2070171>. Elías, A., Jiménez, R., & Shang, H. L. (2023), "Depth-based reconstruction method for incomplete functional data", Computational Statistics, <doi:10.1007/s00180-022-01282-9>. Elías, A., Nagy, S. (2024), "Statistical properties of partially observed integrated functional depths", TEST, <doi:10.1007/s11749-024-00954-6>.

Antonio El<c3><ad>as

fdaPOIFD

Partially Observed Integrated Functional Depth

Antonio Elías

Raúl Jiménez

Anna M. Paganoni

Laura M. Sangalli

Stanislav Nagy

Maximiliam Ofner

learningK function

<dl><dt>data</dt>
<dd>Data matrix.</dd>
<dt>i</dt>
<dd>Index of curve.</dd>
<dt>J</dt>
<dd>Envelope.</dd></dl>

Arguments

Learning k — learningK

<dl>

<dt>data</dt>
<dd>Data matrix.</dd>


<dt>i</dt>
<dd>Index of curve.</dd>


<dt>J</dt>
<dd>Envelope.</dd>

</dl>

learningK: Learning k

Description

Usage

Value

Arguments