White Papers

RE-EM Trees: A New Data Mining Approach for Longitudinal Data

Category: Data Management

Tags: data

Overview Longitudinal data refer to the situation where repeated observations are available for each sampled individual. Methodologies that take this structure into account allow for systematic differences between individuals that are not related to covariates. A standard methodology in the statistics literature for this type of data is the random effects model, where these differences between individuals are represented by so-called "Effects" that are estimated from the data. This paper presents a methodology that combines the flexibility of tree-based estimation methods with the structure of random effects models for longitudinal data.

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Publisher
New York University
File Format
PDF
Date Published
Aug 29, 2009
Format
White Papers
Topics
Knowledge and Data Management, Data Mining - Analysis

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