Mixed-effects random forest for clustered data
Journal of Statistical Computation and Simulation, 2014•Taylor & Francis
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.
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.
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.
Taylor & Francis Online