자료유형  

 학위논문

Control Number  

0017161826

International Standard Book Number  

9798382785110

Dewey Decimal Classification Number  

310

Main Entry-Personal Name  

Cammarata, Louis Vincent.

Publication, Distribution, etc. (Imprint  

[S.l.] : Harvard University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

322 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

General Note  

Advisor: Ke, Zheng Tracy;Uhler, Caroline.

Dissertation Note  

Thesis (Ph.D.)--Harvard University, 2024.

Summary, Etc.  

요약This thesis presents three self-contained chapters: a global detection test in the mixed membership Stochastic Block Model (SBM), a community estimation algorithm in the dynamic degree-corrected mixed membership SBM, and a network-based study of the transcriptional control of cellular signal sensing.Detecting the presence of structured communities is one of the most fundamental problems of statistical network analysis. In Chapter 1, we introduce a degree- and cycle count-based test statistic for global testing in the mixed-membership SBM, a common model for social networks. We derive its asymptotic null distribution and show that it is optimal for all choices of model parameters.Studying the evolving structure of complex dynamic networks is becoming increasingly popular. In Chapter 2, we propose a spectral algorithm for dynamic node embedding and mixed membership estimation. We establish explicit error rates under smoothness assumptions on the temporal evolution of mixed memberships and potentially severe degree heterogeneity, showing that our method is rate optimal across a broad parameter range. We showcase its effectiveness on a trade network and a human contact network.Network methods provide critical insights in many scientific disciplines, such as genomics. In Chapter 3, we report evidence of an important layer of transcriptional control of cellular signal sensing. Taking adhesion receptors as an example, we apply diverse network and statistical approaches to characterize the link between chromatin organization in the cell nucleus, gene co-regulation, and receptor proteins clustering.

Subject Added Entry-Topical Term  

Statistics.

Subject Added Entry-Topical Term  

Bioinformatics.

Subject Added Entry-Topical Term  

Genetics.

Index Term-Uncontrolled  

Community estimation

Index Term-Uncontrolled  

Genomic networks

Index Term-Uncontrolled  

Global detection

Index Term-Uncontrolled  

Network science

Added Entry-Corporate Name  

Harvard University Statistics

Host Item Entry  

Dissertations Abstracts International. 85-12B.

Electronic Location and Access  

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Control Number  

joongbu:657461