当数据被采样或丢失时,网络人口的建模。

IF 0.7 Q3 STATISTICS & PROBABILITY
Ian E Fellows, Mark S Handcock
{"title":"当数据被采样或丢失时,网络人口的建模。","authors":"Ian E Fellows,&nbsp;Mark S Handcock","doi":"10.1007/s40300-023-00246-3","DOIUrl":null,"url":null,"abstract":"<p><p>Networked populations consist of inhomogeneous individuals connected via relational ties. The individuals typically vary in multivariate attributes. In some cases primary interest focuses on individual attributes and in others the understanding of the social structure of the ties. In many circumstances both are of interest, as is their relationship. In this paper we consider this last, most general, case. We model the joint distribution of social ties and individual attributes when the population is only partially observed. Of central interest is when the population is surveyed using a network sampling design. A second situation is when data about a subset of the ties and/or the individual attributes is unintentionally missing. Exponential-family random network models (ERNM)s are capable of specifying a joint statistical representation of both the ties of a network and individual attributes. This class of models allow the nodal attributes to be modeled as stochastic processes, expanding the range and realism of exponential-family approaches to network modeling. In this paper we develop a theory of inference for ERNMs when only part of the network is observed, as well as specific methodology for partially observed networks, including non-ignorable mechanisms for network-based sampling designs. In particular, we consider data collected via contact tracing, of considerable importance to infectious disease epidemiology and public health.</p>","PeriodicalId":51716,"journal":{"name":"Metron-International Journal of Statistics","volume":"81 1","pages":"21-35"},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199300/pdf/","citationCount":"1","resultStr":"{\"title\":\"Modeling of networked populations when data is sampled or missing.\",\"authors\":\"Ian E Fellows,&nbsp;Mark S Handcock\",\"doi\":\"10.1007/s40300-023-00246-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Networked populations consist of inhomogeneous individuals connected via relational ties. The individuals typically vary in multivariate attributes. In some cases primary interest focuses on individual attributes and in others the understanding of the social structure of the ties. In many circumstances both are of interest, as is their relationship. In this paper we consider this last, most general, case. We model the joint distribution of social ties and individual attributes when the population is only partially observed. Of central interest is when the population is surveyed using a network sampling design. A second situation is when data about a subset of the ties and/or the individual attributes is unintentionally missing. Exponential-family random network models (ERNM)s are capable of specifying a joint statistical representation of both the ties of a network and individual attributes. This class of models allow the nodal attributes to be modeled as stochastic processes, expanding the range and realism of exponential-family approaches to network modeling. In this paper we develop a theory of inference for ERNMs when only part of the network is observed, as well as specific methodology for partially observed networks, including non-ignorable mechanisms for network-based sampling designs. In particular, we consider data collected via contact tracing, of considerable importance to infectious disease epidemiology and public health.</p>\",\"PeriodicalId\":51716,\"journal\":{\"name\":\"Metron-International Journal of Statistics\",\"volume\":\"81 1\",\"pages\":\"21-35\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199300/pdf/\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metron-International Journal of Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s40300-023-00246-3\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/5/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"STATISTICS & PROBABILITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metron-International Journal of Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40300-023-00246-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/5/20 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 1

摘要

网络人口由通过关系纽带连接的非同质个体组成。个体的多元属性通常各不相同。在某些情况下,主要兴趣集中在个人属性上,而在另一些情况下,则集中在对关系的社会结构的理解上。在许多情况下,双方都感兴趣,他们的关系也是如此。在本文中,我们考虑最后一个,也是最普遍的情况。当只部分观察到人口时,我们对社会关系和个人属性的联合分布进行了建模。最感兴趣的是使用网络抽样设计对人群进行调查。第二种情况是,有关关系的子集和/或单个属性的数据无意中丢失。指数族随机网络模型能够指定网络关系和单个属性的联合统计表示。这类模型允许将节点属性建模为随机过程,扩展了网络建模的指数族方法的范围和现实性。在本文中,我们开发了一种仅观察到部分网络时ERMM的推理理论,以及部分观察到的网络的具体方法,包括基于网络的采样设计的不可忽略机制。特别是,我们认为通过接触者追踪收集的数据对传染病流行病学和公共卫生具有相当重要的意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Modeling of networked populations when data is sampled or missing.

Modeling of networked populations when data is sampled or missing.

Modeling of networked populations when data is sampled or missing.

Modeling of networked populations when data is sampled or missing.

Networked populations consist of inhomogeneous individuals connected via relational ties. The individuals typically vary in multivariate attributes. In some cases primary interest focuses on individual attributes and in others the understanding of the social structure of the ties. In many circumstances both are of interest, as is their relationship. In this paper we consider this last, most general, case. We model the joint distribution of social ties and individual attributes when the population is only partially observed. Of central interest is when the population is surveyed using a network sampling design. A second situation is when data about a subset of the ties and/or the individual attributes is unintentionally missing. Exponential-family random network models (ERNM)s are capable of specifying a joint statistical representation of both the ties of a network and individual attributes. This class of models allow the nodal attributes to be modeled as stochastic processes, expanding the range and realism of exponential-family approaches to network modeling. In this paper we develop a theory of inference for ERNMs when only part of the network is observed, as well as specific methodology for partially observed networks, including non-ignorable mechanisms for network-based sampling designs. In particular, we consider data collected via contact tracing, of considerable importance to infectious disease epidemiology and public health.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Metron-International Journal of Statistics
Metron-International Journal of Statistics STATISTICS & PROBABILITY-
CiteScore
1.60
自引率
0.00%
发文量
11
期刊介绍: METRON welcomes original articles on statistical methodology, statistical applications, or discussions of results achieved by statistical methods in different branches of science.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信