Time series analysis and its applications

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In recent studies of temporal profiling of gene expression patterns, thousands time series gene expression data are available simultaneously. Such massive datasets are demanding novel approaches to handling difficult issues resulting from substantial model selections and classifications. With a focus on these challenges, my research will concentrate on improving the Bayes information selection criterion resulting in increased efficiency and flexibility. Another area of my research will engage in incorporating robustness of nonparametric rank methods with short time series analysis and applying it to areas such as clustering and testing trends in short times series. The motivation for this work comes largely from my consulting research with a medication compliance study. It is hoped that the results from this study will bring about a significant impact on health research areas, genomewide studies as well as environmental time series analysis. Among other topics, my proposed research will also be dedicated to the study of powerful unit root tests for detecting nonstationarity in microeconomic time series.