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Statistical methods for indirectly observed network data.

机译：间接观察到的网络数据的统计方法。

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摘要

Social networks have become an increasingly common framework for understanding and explaining social phenomena. Yet, despite an abundance of sophisticated models, social network research has yet to realize its full potential, in part because of the difficulty of collecting social network data. In many cases, particularly in the social sciences, collecting complete network data is logistically and financially challenging. In contrast, Aggregated Relational Data (ARD) measure network structure indirectly by asking respondents how many connections they have with members of a certain subpopulation (e.g. How many individuals with HIV/AIDS do you know?). These data require no special sampling procedure and are easily incorporated into existing surveys. This research develops a latent space model for ARD.;This dissertation proposes statistical methods for methods for estimating social network and population characteristics using one type of social network data collected using standard surveys. First, a method to estimate both individual social network size (i.e., degree) and the distribution of network sizes in a population is prosed. A second method estimates the demographic characteristics of hard-to-reach groups, or latent demographic profiles. These groups, such as those with HIV/AIDS, unlawful immigrants, or the homeless, are often excluded from the sampling frame of standard social science surveys. A third method develops a latent space model for ARD. This method is similar in spirit to previous latent space models for networks (see Hoff, Raftery and Handcock (2002), for example) in that the dependence structure of the network is represented parsimoniously in a multidimensional geometric space. The key distinction from the complete network case is that instead of conditioning on the (latent) distance between two members of the network, the latent space model for ARD conditions on the expected distance between a survey respondent and the center of a subpopulation in the latent space. A spherical latent space facilitates tractable computation of this expectation. This model estimates relative homogeneity between groups in the population and variation in the propensity for interaction between respondents and group members.
机译：社交网络已成为理解和解释社会现象的越来越普遍的框架。然而，尽管有许多复杂的模型，但社交网络研究尚未充分发挥其潜力，部分原因是收集社交网络数据很困难。在许多情况下，尤其是在社会科学领域，收集完整的网络数据在后勤和财务上都具有挑战性。相反，汇总关系数据（ARD）通过询问受访者与某个特定人群的成员之间有多少联系（例如，您知道有多少艾滋病毒/艾滋病患者？）来间接测量网络结构。这些数据不需要特殊的采样程序，可以轻松地合并到现有调查中。本研究开发了一种潜在的ARD的空间模型。本文提出了一种统计方法，用于使用标准调查收集的一种类型的社交网络数据估算社交网络和人口特征的方法。首先，提出了一种既估计个体社交网络规模（即程度）又估计人口中网络规模分布的方法。第二种方法估计难以到达的群体的人口特征或潜在的人口概况。这些群体，例如艾滋病毒/艾滋病，非法移民或无家可归者，通常被排除在标准社会科学调查的抽样框架之外。第三种方法开发了ARD的潜在空间模型。这种方法在本质上与以前的网络潜在空间模型（例如，参见Hoff，Raftery和Handcock（2002））相似，因为网络的依存结构在多维几何空间中被简约地表示。与完整网络案例的主要区别在于，不是以网络的两个成员之间的（潜在）距离为条件，而是以ARD条件为基础的ARD潜在空间模型取决于调查对象与潜在人口中子中心之间的预期距离空间。球形的潜在空间有助于对该期望值进行易于处理的计算。该模型估计了人口群体之间的相对同质性，以及受访者和群体成员之间互动倾向的变化。

著录项

  • 作者

    McCormick, Tyler H.;

  • 作者单位

    Columbia University.;

  • 授予单位 Columbia University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 150 p.
  • 总页数 150
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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