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Abstract:
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Classification and regression tree (CART) has been broadly applied due to its simplicity of explanation, automatic variable selection, visualization and interpretation. Previous algorithms for constructing CART for longitudinal data suffer from the computational difficulties in estimation of covariance matrix at each node. We proposed to utilize the quadratic inference function (QIF) and developed a new criterion, named RSSQ, to select the best splits. The proposed approach incorporates correlation wihout estimating the correlation parameters. Therefore we could improve the efficiency of the partition results and prediction accuracy.
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