This work gives a contribution to the emerging literature on the use of regression trees for hierarchical data to increase the flexibility and the predictive ability of random effects models. The proposed procedure extends random effect re- gression trees considering a random effect model with both a tree component and a linear component. Moreover, it is suggested to decompose the effects of predictors within and between clusters. The performance of the proposed procedure is evaluated through a simulation study and an application to INVALSI data on students achieve- ment.
Tree embedded linear mixed models / Gottard, Anna; Gottard, Anna; Grilli, Leonardo; Grilli, Leonardo; Rampichini, Carla; Rampichini, Carla; Vannucci, Giulia; Vannucci, Giulia. - (2019), pp. 239-242. (Intervento presentato al convegno Cladag 2019 tenutosi a Cassino nel 11-13 Settembre 2019).
Tree embedded linear mixed models
Giulia Vannucci;Giulia Vannucci
2019
Abstract
This work gives a contribution to the emerging literature on the use of regression trees for hierarchical data to increase the flexibility and the predictive ability of random effects models. The proposed procedure extends random effect re- gression trees considering a random effect model with both a tree component and a linear component. Moreover, it is suggested to decompose the effects of predictors within and between clusters. The performance of the proposed procedure is evaluated through a simulation study and an application to INVALSI data on students achieve- ment.File | Dimensione | Formato | |
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