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abstract: The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation. / Dissertation/Thesis / Ph.D. Industrial Engineering 2011

證券市場和一般商品市場本質上有所不同; 一般商品市場的需求和供給者是截然劃分 的集團; 而證券市場是一個流通市場, 其參與者既是需求者亦是供給者, 以經濟學理 論來預測分析, 解釋證券市場行為是一種主流, 而價格和數量又是經濟學領域中最主 要兩個變數; 環視現代探討證券市場行為的文章, 大部份只偏重於價格或數量單方面 之探討; 或價格與數量間單方向因果關系的研究, 由這些研究所得結論來說明證券市 場價格和數量間關系顯然不夠, 例如在探討未來價格變動時除了前期價格因素外, 尚 有數量因素會影響未來價格因素的發展。 多元時間序列分析方法系直接使用多個變數數列資料間所顯示之自我相關特性及交叉 相關特性以設定出變數間可能存在的因果關系, 而且其具有以下之優點。(1) 序列與 序列之間可能存在領先、同時、及回饋等多種關系, 藉著MARMA 模型之設定即可顯示 多個序列間基本之動態關系。(2) 聯合多個數列來建立模型亦可利用其他數列所提供 情報提高預測之準確性。(3) 介之分析(Intervention Analysis) 或季節因素之調整 都可由MARMA 模型之建之得到更精確的結果。 本文即利用多元時間序列模式(Multiple Time Series Models簡稱MARMA)分析方法, 探討證券市場價格與數量同時對數量或同時對價格的影響。