This paper proposes a novel likelihood-based boosting method for the selection of the random effects in linear mixed models. The nonconvexity of the objective function to minimize, which is the negative profile log-likelihood, requires the adoption of new solutions. In this respect, our optimization approach also employs the directions of negative curvature besides the usual Newton directions. A simulation study and a real-data application show the good performance of the proposal.
A boosting method to select the random effects in linear mixed models
Battauz, Michela
;Vidoni, Paolo
2024-01-01
Abstract
This paper proposes a novel likelihood-based boosting method for the selection of the random effects in linear mixed models. The nonconvexity of the objective function to minimize, which is the negative profile log-likelihood, requires the adoption of new solutions. In this respect, our optimization approach also employs the directions of negative curvature besides the usual Newton directions. A simulation study and a real-data application show the good performance of the proposal.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
ujae010.pdf
non disponibili
Licenza:
Non pubblico
Dimensione
656.77 kB
Formato
Adobe PDF
|
656.77 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
