Robustness of Linear Mixed Models (LMM) with random effects is investigated with the forward search (FS). Extending the FS to LMM offers new computational challenges, as some restrictions, imposed by the model and their estimates, are required. The method is illustrated by an application to real data where exports of coffee to European Union are analyzed to identify outliers that might be linked to potential frauds. An additional short simulation is presented to strengthen the usefulness of the proposed method.
Robust Diagnostics for Linear Mixed Models with the Forward Search
Grossi, L;
2023
Abstract
Robustness of Linear Mixed Models (LMM) with random effects is investigated with the forward search (FS). Extending the FS to LMM offers new computational challenges, as some restrictions, imposed by the model and their estimates, are required. The method is illustrated by an application to real data where exports of coffee to European Union are analyzed to identify outliers that might be linked to potential frauds. An additional short simulation is presented to strengthen the usefulness of the proposed method.
File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
pagine da BridgeGapValladolid.pdf
non disponibili
Tipologia:
Published (publisher's version)
Licenza:
Accesso privato - non pubblico
Dimensione
2.72 MB
Formato
Adobe PDF
|
2.72 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
