Document Detail


Regularized sandwich estimators for analysis of high-dimensional data using generalized estimating equations.
MedLine Citation:
PMID:  20528857     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Summary A modification of generalized estimating equations (GEEs) methodology is proposed for hypothesis testing of high-dimensional data, with particular interest in multivariate abundance data in ecology, an important application of interest in thousands of environmental science studies. Such data are typically counts characterized by high dimensionality (in the sense that cluster size exceeds number of clusters, n>K) and over-dispersion relative to the Poisson distribution. Usual GEE methods cannot be applied in this setting primarily because sandwich estimators become numerically unstable as n increases. We propose instead using a regularized sandwich estimator that assumes a common correlation matrix R, and shrinks the sample estimate of R toward the working correlation matrix to improve its numerical stability. It is shown via theory and simulation that this substantially improves the power of Wald statistics when cluster size is not small. We apply the proposed approach to study the effects of nutrient addition on nematode communities, and in doing so discuss important issues in implementation, such as using statistics that have good properties when parameter estimates approach the boundary (), and using resampling to enable valid inference that is robust to high dimensionality and to possible model misspecification.
Authors:
David I Warton
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Biometrics     Volume:  67     ISSN:  1541-0420     ISO Abbreviation:  Biometrics     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-03-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0370625     Medline TA:  Biometrics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  116-23     Citation Subset:  IM    
Copyright Information:
© 2010, The International Biometric Society.
Affiliation:
School of Mathematics and Statistics and Evolution and Ecology Research Centre, The University of New South Wales, NSW 2052, Australia.
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