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Annie Qu
Associate Professor, Department of Statistics
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RESEARCH:
Qu's main research focuses on statistical method for the analysis of correlated data using the quadratic inference function approach. In particular on developing statistical theory and practical methodology for complex high dimensional correlated data where the full parametric likelihood function of the model is difficult to specify, and partial data information is not accurate or is missing.
Qu's other research interests include cell cycle microarray data where gene expressions are measured over time. There are two kinds of correlations in cell cycle data. Measurements are certainly correlated within a gene where it is measured over cycles, and measurements could also be correlated between genes since some genes are biologically related and regulate the same phenotypical characteristics. Notice that there are many gene-specific parameters involved in the model, but often there are no replications of measurements. Qu and her former student Dr. Tsai developed a semi-nonparametric model which reduces the dimension of the nuisance parameters (parameters not related to the treatment) and still addresses the correlated nature of the measurements.