calculate_obj_for_g

Helpfunction in update_g().
Depends on an not yet established group k ( cannot use lgfg_list)

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

calculate_obj_for_g function

<dl><dt>i</dt>
<dd>individual</dd>
<dt>k</dt>
<dd>group</dd>
<dt>errors_virtual</dt>
<dd>list with errors for each possible group</dd>
<dt>rho_parameters</dt>
<dd>median and madn of the calculated error term</dd>
<dt>robust</dt>
<dd>robust or classical estimation</dd>
<dt>TT</dt>
<dd>length of time series</dd></dl>

Arguments

Calculates objective function for individual i and group k in order to estimate group membership. — calculate_obj_for_g

<dl>

<dt>i</dt>
<dd>individual</dd>


<dt>k</dt>
<dd>group</dd>


<dt>errors_virtual</dt>
<dd>list with errors for each possible group</dd>


<dt>rho_parameters</dt>
<dd>median and madn of the calculated error term</dd>


<dt>robust</dt>
<dd>robust or classical estimation</dd>


<dt>TT</dt>
<dd>length of time series</dd>

</dl>

calculate_obj_for_g: Calculates objective function for individual i and group k in order to estimate group membership.

Description

Usage

Value

Arguments