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Mixed-effects random forest for clustered data

Hajjem, Ahlem ; Bellavance, François ; Larocque, Denis

Journal of statistical computation and simulation, 2014-06, Vol.84 (6), p.1313-1328 [Periódico revisado por pares]

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Título:
Mixed-effects random forest for clustered data
Autor: Hajjem, Ahlem ; Bellavance, François ; Larocque, Denis
Assuntos: Algorithms ; clustered data ; Clustering ; Computation ; Computer simulation ; Forests ; mixed effects ; Offices ; random forest ; Randomized algorithms ; Regression analysis ; regression tree ; Revenues ; Simulation
É parte de: Journal of statistical computation and simulation, 2014-06, Vol.84 (6), p.1313-1328
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Descrição: This paper presents an extension of the random forest (RF) method to the case of clustered data. The proposed 'mixed-effects random forest' (MERF) is implemented using a standard RF algorithm within the framework of the expectation-maximization algorithm. Simulation results show that the proposed MERF method provides substantial improvements over standard RF when the random effects are non-negligible. The use of the method is illustrated to predict the first-week box office revenues of movies.
Editor: Abingdon: Taylor & Francis
Idioma: Inglês

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