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Activity Number: 545
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #306237
Title: Outputation Permutation: Exact Inference for Complex Clustered Data
Author(s): Dean A. Follmann*+ and Michael P. Fay
Companies: National Institute of Allergy and Infectious Diseases and National Institute of Allergy and Infectious Diseases
Address: , Bethesda, MD, ,
Keywords: correlated data ; clustering ; permutation test ; within cluster resampling
Abstract:

This talk introduces exact permutation methods for use when there are independent clusters of data with arbitrary within cluster correlation. To eliminate the problem of clustering, we randomly select a data point from each cluster and for this now independent data, calculate our test statistic and the associated support points for all possible permutations. While clearly valid, this is also inefficient. We repeat this process until all possible independent data sets have been created and use the averaged (over each unique permutation) support points as our reference distribution for the averaged test statistic. This approach is similar in spirit to the within cluster resampling of Hoffman, Sen, \& Weinberg (2001), or multiple outputation of Follmann, Proschan \& Leifer (2003). We both exact and Monte Carlo version of this idea.

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