determine_robust_lambda

Uses the "almost classical lambda" (=matrix where the mean of each row is equal to the classical lambda) to create a robust lambda by using M estimation.

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

determine_robust_lambda function

<dl><dt>almost_classical_lambda</dt>
<dd>matrix where the mean of each row is equal to the classical lambda</dd>
<dt>fastoption</dt>
<dd>Uses nlm() instead of optim(). This is faster.</dd>
<dt>fastoption2</dt>
<dd>experimental parameter: can speed nlm() up (10%), but loses accuracy. May benefit from finetuning.</dd></dl>

Arguments

Help-function for return_robust_lambdaobject(). — determine_robust_lambda

<dl>

<dt>almost_classical_lambda</dt>
<dd>matrix where the mean of each row is equal to the classical lambda</dd>


<dt>fastoption</dt>
<dd>Uses nlm() instead of optim(). This is faster.</dd>


<dt>fastoption2</dt>
<dd>experimental parameter: can speed nlm() up (10%), but loses accuracy. May benefit from finetuning.</dd>

</dl>

determine_robust_lambda: Help-function for return_robust_lambdaobject().

Description

Usage

Value

Arguments