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Abstract Details
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Activity Number:
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667
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics and the Environment
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| Abstract - #305892 |
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Title:
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Incorporating Random Effects Into Classification and Regression Trees with Correlated Binary Data
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Author(s):
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Mark McKelvey*+ and Philip Dixon
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Companies:
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Iowa State University and Iowa State University
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Address:
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2313 Georgetown Place, Bellevue, NE, 68123, United States
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Keywords:
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CART ;
correlated binary data ;
occupancy
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Abstract:
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Classification and regression trees (CART) are a flexible, frequently-used method for modeling probabilities of events. Many studies include a cluster-type sampling design where there is a clear spatial correlation between sampling locations. This correlation causes the variance of the node occupancy estimates in CART to be biased. We suggest a generalized estimating equation (GEE)-based approach in which the naïve variance estimates (calculated as if all locations were independent) are "corrected" based on the data available in each parent node of the tree. The corrected variance estimates are then used to revise the binary-split decision criterion of the tree. The variances of each node in the split are assumed to be unequal. We demonstrate this method using data from a study on bird occurrences in Oregon.
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