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Semiparametric marginal and mixed models for longitudinal data



Graduation date: 2006This thesis consists of three papers which investigate marginal models,\ud nonparametric approaches, generalized mixed effects models and variance\ud components estimation in longitudinal data analysis.\ud In the first paper, a new marginal approach is introduced for high-dimensional\ud cell-cycle microarray data with no replicates. There are two\ud kinds of correlation for cell-cycle microarray data. Measurements within a\ud gene are correlated and measurements between genes are also correlated since\ud some genes may be biologically related. The proposed procedure combines\ud a classifying method, quadratic inference function method and nonparametric\ud techniques for complex high dimensional cell cycle microarray data. The\ud gene classifying method is first applied to identify genes with similar cell cycle\ud patterns into the same class. Then we use genes within the same group\ud as pseudo-replicates to fit a nonparametric model. The quadratic inference\ud function is applied to incorporate within-gene correlations. An asymptotic\ud chi-squared test is also applied to test whether certain genes have cell cycles \ud phenomena. Simulations and an example of cell-cycle microarray data are\ud illustrated.\ud The second paper proposes a new approach for generalized linear mixed\ud models in longitudinal data analysis. This new approach is an extension of\ud the quadratic inference function (Qu et al., 2000) for generalized linear mixed\ud models. Two conditional extended scores are constructed for estimating fixed\ud effects and random effects. This new approach involves only the first and second\ud conditional moments. It does not require the specification of a likelihood\ud function and also takes serial correlations of errors into account. In addition,\ud the estimation of unknown variance components associated with random\ud effects or nuisance parameters associated with working correlations are not required.\ud Furthermore, it does not require the normality assumption for random\ud effects.\ud In the third paper, we develop a new approach to estimate variance\ud components using the second-order quadratic inference function. This is an\ud extension of the quadratic inference function for variance components estimation\ud in linear mixed models. The new approach does not require the specification\ud of a likelihood function. In addition, we propose a chi-squared test to test\ud whether the variance components of interest are significant. This chi-squared\ud test can also be used for testing whether the serial correlation is significant.\ud Simulations and a real data example are provided as illustration

Year: 2005
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Provided by: ScholarsArchive@OSU
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