CAREER: Semiparametric and Non-Parametric Models for Correlated Data

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CAREER: Semiparametric and nonparametric models for correlated dataAbstract:This research project is aimed at developing statistical theory andpractical methodology for complex high dimensional correlated datawhere the full parametric likelihood function of the model is difficultto specify or intractable, and partial data information is not accurateor is missing. The PI and her collaborators will developefficient and robust estimation procedures by incorporatingcorrelation structures into the models where high dimensionalnuisance parameters are present, and develop inference functionsfor hypothesis testing with low computational intensity.Part of research goals for the 5-year plan are:to provide an explicit maximum number of contaminatedclusters allowed to maintain the consistency of the estimator usingquadratic inference functions; to develop unbiased and efficientestimating functions if missing responses are missing at random,and inference functions for testing the model assumption;to develop an efficient esimator using a nonparametric regressionspline with relatively low demand on computation, and introduce agoodness-of-fit test with a chi-squared property for testing whethercoefficients in nonparametric regression are time-varying or timeinvariant; and, to develop semi-nonparametric models for cell cyclemicroarray data to incorporate both temporal correlation within genesand correlation between biologically related genes.This research will have significant impact and many applications inbiomedical research, econometrics, environmental studies, oceanography,social science and public health where correlated data ariseoften. The outlined research projects help to tackle fundamentalquestions in statistical science and will stimulate interest from alarge group of scientists. It also makes connections betweentheory and methods developed in econometrics, statistics andbiostatistics. The proposed research will benefit biomedical researchto help combat life threatening diseases such as AIDS and cancer,and will make contributions to identifying cell cycle regulated genesmore accurately. It will integrate current states of knowledge ofproposed research areas substantially into educational activitiesthrough development ofnew courses on nonparametric methods and microarray data analysis.It will advance undergraduate and graduate students' learning andtraining in semiparametric and nonparametric methods. Furthermore,it will broaden opportunities and enable theparticipation of all citizens from various disciplines, includingunderrepresented minorities and international partnerships.