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Combinatorial Inference for Large-Sc...

Lu, Junwei.

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Combinatorial Inference for Large-Scale Data Analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Combinatorial Inference for Large-Scale Data Analysis./
作者:	Lu, Junwei.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	237 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.
Contained By:	Dissertation Abstracts International79-10B(E).
標題:	Statistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10813738
ISBN:	9780438047709

Combinatorial Inference for Large-Scale Data Analysis.
Lu, Junwei.

Combinatorial Inference for Large-Scale Data Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 237 p.

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B.

Thesis (Ph.D.)--Princeton University, 2018.

Problems of inferring the combinatorial structures of networks arise in many real applications ranging from genomic regulatory networks, brain networks to social networks. This poses new and challenging problems on the uncertainty assessment and replicability analysis for statistical methods inferring these network topological structures. This thesis develops combinatorial inference, a new inferential framework for networks, to conduct hypothesis tests and variable selection for combinatorial structures in large-scale graphs.

ISBN: 9780438047709Subjects--Topical Terms:

517247
Statistics.

Combinatorial Inference for Large-Scale Data Analysis.
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520 $a Problems of inferring the combinatorial structures of networks arise in many real applications ranging from genomic regulatory networks, brain networks to social networks. This poses new and challenging problems on the uncertainty assessment and replicability analysis for statistical methods inferring these network topological structures. This thesis develops combinatorial inference, a new inferential framework for networks, to conduct hypothesis tests and variable selection for combinatorial structures in large-scale graphs.
520 $a In the first part of the thesis, we propose a unified inferential method to test hypotheses on the global combinatorial properties of graphical models. We showed that my method works for general monotone graph properties that can be preserved under edge deletion including bipartite, planar, k-colorable, etc. We develop a new combinatorial minimax theory to justify the optimality of the skip-down algorithm. We introduce a new notion of graph packing entropy to sharply characterize the complexity of combinatorial inference problems for graphical models. It can be viewed as the combinatorial counterpart of the famous metric entropy theory on parametric minimax lower bounds.
520 $a In the second part of the thesis, we generalize the combinatorial inference for larger family of graphical models. We propose a novel class of dynamic nonparanormal graphical models, which allows us to model high dimensional heavy-tailed systems and the evolution of their latent network structures. Under this model we develop statistical tests for presence of edges both locally at a fixed index value and globally over a range of values. The tests are developed for a high-dimensional regime, are robust to model selection mistakes and do not require commonly assumed minimum signal strength. The testing procedures are based on a high dimensional, debiasing-free moment estimator, which uses a novel kernel smoothed Kendall's tau correlation matrix as an input statistic.
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