clustering_with_robust_distances

It starts with defining a robust location and scatter (based on Ma &amp; Genton (2000): Highly robust estimation of the autocovariance function).

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework.
The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta.
This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers.
The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along
with their loadings. The number of groups and factors can be estimated if they are unknown.

Ewoud Heyndels

RCTS

Clustering Time Series While Resisting Outliers

clustering_with_robust_distances function

<dl><dt>g</dt>
<dd>Vector with group membership for all individuals</dd>
<dt>number_of_groups</dt>
<dd>number of groups</dd>
<dt>Y</dt>
<dd>Y: the panel data of interest</dd></dl>

Arguments

Function that puts individuals in a separate "class zero", when their distance to all possible groups is bigger then a certain threshold. — clustering_with_robust_distances

<dl>

<dt>g</dt>
<dd>Vector with group membership for all individuals</dd>


<dt>number_of_groups</dt>
<dd>number of groups</dd>


<dt>Y</dt>
<dd>Y: the panel data of interest</dd>

</dl>

clustering_with_robust_distances: Function that puts individuals in a separate "class zero", when their distance to all possible groups is bigger then a certain threshold.

Description

Usage

Value

Arguments