In this dissertation, the generalized score tests based on weighted estimating equations
are proposed for missing covariate data. Their properties, including the effects
of nuisance functions on the forms of the test statistics and efficiency of the tests,
are investigated. Different versions of the test statistic are properly defined for various
parametric and semiparametric settings. Their asymptotic distributions are also
derived. It is shown that when models for the nuisance functions are correct, appropriate
test statistics can be obtained via plugging the estimates of the nuisance
functions into the appropriate test statistic for the case that the nuisance functions
are known. Furthermore, the optimal test is obtained using the relative efficiency
measure. As an application of the proposed tests, a formal model validation procedure
is developed for generalized linear models in the presence of missing covariates.
The asymptotic distribution of the data driven methods is provided. A simulation
study in both linear and logistic regressions illustrates the applicability and the finite
sample performance of the methodology. Our methods are also employed to analyze
a coronary artery disease diagnostic dataset.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-1625 |
| Date | 15 May 2009 |
| Creators | Jin, Lei |
| Contributors | Wang, Suojin |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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