Unconditional estimating equation approaches for missing data Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/7d278w83r

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  • Missing data can lead to biased and inefficient estimation if the missing mechanism is not taken into account in the analysis. In this dissertation we propose two estimators that, under fairly general conditions, are asymptotically unbiased. The first proposed estimator assume the data are missing at random (MAR) and does not require a model for the missing mechanism. The second estimator allows the missingness to be nonignorable and requires a model for the mechanism. Both proposed approaches utilize generalized estimating equations (GEE) based on unconditional models. One main advantage of the proposed approaches is that they do not require full specification of the likelihood. They only need the first few moments of the response variables and covariates. Another advantage is that they can easily handle arbitary missing patterns. Using simulation, we investigate the efficiency of the proposed approaches relative to the weighted GEE (WEE) and multiple imputation (MI) estimators. The proposed estimators are as efficient as WEE and MI estimators when the latter two approaches use the correct model to obtain weights or impute missing values.
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  • description.provenance : Submitted by Lin Lu (luli@onid.orst.edu) on 2007-12-30T05:08:22Z No. of bitstreams: 1 main.pdf: 360388 bytes, checksum: a7bde8ae8800710f7296294ffab2655b (MD5)
  • description.provenance : Approved for entry into archive by Julie Kurtz(julie.kurtz@oregonstate.edu) on 2008-01-10T18:08:03Z (GMT) No. of bitstreams: 1 main.pdf: 360388 bytes, checksum: a7bde8ae8800710f7296294ffab2655b (MD5)

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