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Title: Learning with Asymmetry, High Dimension and Social Networks
Authors: Tong, Xin
Advisors: Fan, Jianqing
Rigollet, Philippe
Contributors: Operations Research and Financial Engineering Department
Keywords: High Dimension
Social Network
Subjects: Statistics
Computer science
Issue Date: 2012
Publisher: Princeton, NJ : Princeton University
Abstract: Yes or no is perhaps the most common answer we provide each day. Indeed, binary answers to well structured questions are the building blocks of our knowledge. I started my research career in drafting such answers in various circumstances within the domains of statistics and machine learning. From statisticians and computer scientists' point of view, classification is a well defined field. But more broadly, discretization is a powerful convention to help us understand the real-world social, economic and scientific situations. Also, the clean and tractable finite sample results from classification literature motivates me to investigate the explicit interplay among parameters in other fields. In this essay, I include my selected works regarding binary status in high dimensional statistics, statistical learning theory and social networks. In the first chapter, I introduce Regularized Optimal Affine Discriminant (ROAD), a high dimensional classification method explicitly using covariance information. In the second chapter, novel performance bounds of oracle type for asymmetric errors under the Neyman-Pearson context are derived. In the third chapter, I study the problem of information aggregation in social networks, where the focus is to determine aggregate learning status in any finite population network.
Alternate format: The Mudd Manuscript Library retains one bound copy of each dissertation. Search for these copies in the library's main catalog
Type of Material: Academic dissertations (Ph.D.)
Language: en
Appears in Collections:Operations Research and Financial Engineering

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