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- W2511190312 abstract "The Generalized Estimating Equations (GEE) procedure ofLiang and Zeger (1986) can be highly influenced by the presence ofunusual data points. Deletion diagnostics are introduced that consider leverage and residuals to measure the influence ofa subset ofobservations on the estimated regression parameters and on the estimated values ofthe linear predictor. Computational formulae are provided which correspond to the influence ofa single observation and ofan entire cluster ofcorrelated observations. The proposed diagnostics are generalizations ofDBETA (Belsley et al. (1980» and Cook's D (Cook (1977» oflinear regression. As an alternative approach, the influence ofobservations is addressed by Resistant Generalized Estimating Equations (REGEE). A generalization of the GEE procedure, REGEE gives parameter estimates and fitted values which are resistant to influential data. Robustness is achieved in REGEE through the inclusion of a diagonal weight matrix for each cluster in the estimating equations which downweight the multivariate response vector element-wise. The weights are defined with respect to leverage, corresponding to the Mallows class (Carroll and Pederson (1993», or residual, the Schweppe class (pregibon (1982». The large sample and small sample properties are studied. An example of medical practice data is given to illustrate the use ofthe deletion diagnostics and REGEE for correlated binary regression." @default.
- W2511190312 created "2016-09-16" @default.
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- W2511190312 date "1995-01-01" @default.
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- W2511190312 title "Regression diagnostics and resistant fits for generalized estimating equations" @default.
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