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Activity Number:
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212
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Type:
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Contributed
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Date/Time:
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Tuesday, August 13, 2002 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section*
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| Abstract - #300865 |
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Title:
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Influence Diagnostics for Correlated Data
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Author(s):
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Martha Nunn*+ and Brian Leroux
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Affiliation(s):
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Boston University and University of Washington
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Address:
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715 Albany B-342, Boston, Massachusetts, 02118, USA
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Keywords:
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influence ; cluster data
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Abstract:
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Correlated or clustered data are frequently encountered in medical and dental research. Although there has been a large amount of research on analysis methods for clustered data, there has been only a limited amount of work in the area of influence diagnostics for these methods. We present a simple approach to influence diagnostics for clustered data that is practical even for very large data sets. By applying a method of partitioning the fixed effects in a linear model with clustered data and exchangeable correlation structure (Scott and Holt, 1982), ordinary least-squares estimation can be used to obtain influence diagnostics with balanced data. Using a series of simulations, we evaluate the performance of the influence diagnostics for a partitioned linear model where data are unbalanced and where cluster deletion is performed as opposed to point deletion. When the correlation structure is exchangeable and the data are unbalanced, these influence measures provide a reasonably high degree of sensitivity for identifying influential observations. This technique may offer a less cumbersome approach to influence diagnostics with clustered data.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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