Biometrika Advance Access published online on January 31, 2008
Biometrika, doi:10.1093/biomet/asm094
Articles |
Diagnostic measures for empirical likelihood of general estimating equations
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420, U.S.A. hzhu{at}bios.unc.edu ibrahim{at}bios.unc.edu
Department of Statistics, Yunnan University, Kunming, China nstang{at}ynu.edu.cn
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520-8034, U.S.A.
Jiangxi Normal University, Nanchang, China heping.zhang{at}yale.edu
Received for publication 1 August 2006. Revision received 1 August 2007.
We develop diagnostic measures for assessing the influence of individual observations when using empirical likelihood with general estimating equations, and we use these measures to construct goodness-of-fit statistics for testing possible misspecification in the estimating equations. Our diagnostics include case-deletion measures, local influence measures and pseudo-residuals. Our goodness-of-fit statistics include the sum of local influence measures and the processes of pseudo-residuals. Simulation studies are conducted to evaluate our methods, and real datasets are analyzed to illustrate the use of our diagnostic measures and goodness-of-fit statistics.
Key Words: Diagnostic measure • Empirical likelihood • Estimating equation • Goodness-of-fit statistic • Resampling method
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