American Statistical Association
Clustered binary data with a large number of covariates have become increasingly common in many scientific disciplines. We consider a generalized estimating equations (GEE) approach to analyzing such data when the number of covariates grows to infinity with the number of clusters. This approach only requires the specification of the first two marginal moment conditions. The likelihood function does not need to be specified or approximated. In the first part of the talk, we consider an extension of the classical theory of GEE to the large n, diverging p framework. We provide appropriate regularity conditions and establish the asymptotic properties of the GEE estimator. In particular, we show that the GEE estimator remains consistent and asymptotically normal, and that the large sample Wald test remains valid even when the working correlation matrix is misspecified.
In the second part of the talk, we propose penalized GEE for simultaneous variable selection and estimation. The properties of the penalized GEE are investigated in the “large n, diverging p” setting. The proposed estimator enjoys an oracle property. The consistency of model selection holds even if the working correlation matrix is misspecified. Furthermore, we propose an effective iterative algorithm to solve the penalized GEE. We demonstrate the new method via simulations and an application to a real data example.
Dr. Lan Wang received her Ph.D. in statistics from Pennsylvania State University in 2003. She is a now an associate professor in School of Statistics, University of Minnesota. Dr. Wang is interested in nonparametric and semiparametric statistics with focus on high-dimensional data analysis, variable selection, quantile regression, estimating equations, censored data, model diagnostics, and their applications.
|Date:||Thursday, February 24, 2011|
|Time:||4:00 - 5:00 P.M.|
Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
Biostatistics Computer Lab
6th Floor - Room 656
New York, New York