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Splitting variable selection for multivariate regression trees

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Abstract

We show that the usual exhaustive search principle adapted for multivariate regression trees has selection bias toward variables with more split points. A selection scheme is proposed to control bias by utilizing hierarchical loglinear model for three-way contingency table of residuals.

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