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Statistical Reasoning in Network Data.
~
Lee, Youjin.
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Statistical Reasoning in Network Data.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Statistical Reasoning in Network Data./
作者:
Lee, Youjin.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:
255 p.
附註:
Source: Dissertations Abstracts International, Volume: 81-07, Section: A.
Contained By:
Dissertations Abstracts International81-07A.
標題:
Statistics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27606785
ISBN:
9781687987143
Statistical Reasoning in Network Data.
Lee, Youjin.
Statistical Reasoning in Network Data.
- Ann Arbor : ProQuest Dissertations & Theses, 2019 - 255 p.
Source: Dissertations Abstracts International, Volume: 81-07, Section: A.
Thesis (Ph.D.)--The Johns Hopkins University, 2019.
This item must not be sold to any third party vendors.
Networks are collections of nodes, which can represent entities like people, genes, or brain regions, and ties between pairs of nodes, which represent various forms of connection, e.g. social relationships, between them. The study of networks is booming in biology, economics, statistics, psychology, physics, computer science, social science, public health, and beyond. Despite the increased interest in network data and its application, methods do not yet exist to answer many types of statistical and causal questions about observations collected from networks.In this dissertation, we illustrate an unacknowledged problem for statistical methods using network data, namely network dependence, and propose a test for the existence of such dependence. We demonstrate how this kind of dependence affects the validity of statistical inference. In particular, one of the most important sources of data on cardiovascular disease epidemiology, the Framingham Heart Study, is shown to exhibit dependence that could lead to false statistical conclusions. We also propose a network dependence test that overcomes the high-dimensional structure of network data.Many researchers interested in social networks in public health and social science are ultimately interested in causal inference on certain collective behaviors or health outcomes observed over the whole network -- such as the causal effect of a certain vaccination plan on the overall rate of infections, or the causal effect of an online viral marketing program on the sales of products. In the last part of the dissertation, we focus on one of those questions that aims to identify the most influential subjects in networks.
ISBN: 9781687987143Subjects--Topical Terms:
517247
Statistics.
Subjects--Index Terms:
Social network
Statistical Reasoning in Network Data.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27606785
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