derpsiOpt

Second derivative of the quasi \(\rho\) function

A clustering algorithm similar to K-Means is implemented, it has two main advantages,
namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when
there are atypical values in the sample and (b) The estimator is efficient, roughly speaking,
if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means).
Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale.
(see Gonzalez, Yohai and Zamar (2019) <arxiv:1906.08198>).

Juan Domingo Gonzalez

ktaucenters

Robust Clustering Procedures

Victor J. Yohai

Ruben H. Zamar

Douglas Alberto Carmona Guanipa

derpsiOpt function

<dl><dt>x</dt>
<dd>numeric vector with positive values.</dd>
<dt>cc</dt>
<dd>tunning constant.</dd></dl>

Arguments

Second derivative of the quasi \(\rho\) function — derpsiOpt

<dl>

<dt>x</dt>
<dd>numeric vector with positive values.</dd>


<dt>cc</dt>
<dd>tunning constant.</dd>

</dl>

derpsiOpt: Second derivative of the quasi \(\rho\) function

Description

Usage

Value

Arguments