Mixed-effects random forest for clustered data

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 p...

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Bibliographic Details
Published inJournal of statistical computation and simulation Vol. 84; no. 6; pp. 1313 - 1328
Main Authors Hajjem, Ahlem, Bellavance, François, Larocque, Denis
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 03.06.2014
Taylor & Francis Ltd
Subjects
Algorithms
clustered data
Clustering
Computation
Computer simulation
Forests
mixed effects
Offices
random forest
Randomized algorithms
Regression analysis
regression tree
Revenues
Simulation
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Summary: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.
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ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2012.741599