In this paper we propose a new Mixed-Effects Quantile Regression Forest by generalizing the Quantile Regression Forest approach to longitudinal data. The inferential procedure is based on the Nonparametric Maximum Likelihood exploiting the Asymmetric Laplace distribution tool. The performance of the ME-QRF is tested in a simulation study and compared with the results of standard quantile regression models. Finally, the ME-QRF is applied to a data set for analysing the effect of the tratment on lead-exposed children.
New advances in Regression Forests
Andreani, Mila
;
2023
Abstract
In this paper we propose a new Mixed-Effects Quantile Regression Forest by generalizing the Quantile Regression Forest approach to longitudinal data. The inferential procedure is based on the Nonparametric Maximum Likelihood exploiting the Asymmetric Laplace distribution tool. The performance of the ME-QRF is tested in a simulation study and compared with the results of standard quantile regression models. Finally, the ME-QRF is applied to a data set for analysing the effect of the tratment on lead-exposed children.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
Andreani_New-advances_2023.pdf
accesso aperto
Tipologia:
Published version
Licenza:
Solo Lettura
Dimensione
1.52 MB
Formato
Adobe PDF
|
1.52 MB | Adobe PDF |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
