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Aggregated relational data (ARD), formed from "How many X's do you know?" questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive…

应用统计 · 统计学 2022-11-03 Ian Laga , Le Bao , Xiaoyue Niu

Collecting complete network data is expensive, time-consuming, and often infeasible. Aggregated Relational Data (ARD), which capture information about a social network by asking a respondent questions of the form ``How many people with…

统计方法学 · 统计学 2022-10-24 Emily Breza , Arun G. Chandrasekhar , Shane Lubold , Tyler H. McCormick , Mengjie Pan

This work is concerned with the estimation of hard-to-reach population sizes using a single respondent-driven sampling (RDS) survey, a variant of chain-referral sampling that leverages social relationships to reach members of a hidden…

Estimating the size of hard-to-reach populations is an important problem for many fields. The Network Scale-up Method (NSUM) is a relatively new approach to estimate the size of these hard-to-reach populations by asking respondents the…

统计方法学 · 统计学 2021-06-04 Ian Laga , Le Bao , Xiaoyue Niu

Population size estimates for hidden and hard-to-reach populations are particularly important when members are known to suffer from disproportion health issues or to pose health risks to the larger ambient population in which they are…

社会与信息网络 · 计算机科学 2018-07-04 Bilal Khan , Hsuan-Wei Lee , Ian Fellows , Kirk Dombrowski

Respondent-Driven Sampling (RDS) is an approach to sampling design and inference in hard-to-reach human populations. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader…

统计方法学 · 统计学 2012-09-28 Mark S. Handcock , Krista J. Gile , Corinne M. Mar

Learning about the social structure of hidden and hard-to-reach populations --- such as drug users and sex workers --- is a major goal of epidemiological and public health research on risk behaviors and disease prevention. Respondent-driven…

社会与信息网络 · 计算机科学 2015-12-03 Lin Chen , Forrest W. Crawford , Amin Karbasi

This study introduces a novel approach for inferring social network structures using Aggregate Relational Data (ARD), addressing the challenge of limited detailed network data availability. By integrating ARD with variational approximation…

计量经济学 · 经济学 2025-09-04 Xunkang Tian

Network surveys of key populations at risk for HIV are an essential part of the effort to understand how the epidemic spreads and how it can be prevented. Estimation of population values from the sample data has been probematical, however,…

应用统计 · 统计学 2019-09-12 Steve Thompson

Estimates of population size for hidden and hard-to-reach individuals are of particular interest to health officials when health problems are concentrated in such populations. Efforts to derive these estimates are often frustrated by a…

社会与信息网络 · 计算机科学 2017-02-01 Bilal Khan , Hsuan-Wei Lee , Kirk Dombrowski

Aggregated Relational Data (ARD) contain summary information about individual social networks and are widely used to estimate social network characteristics and the size of populations of interest. Although a variety of ARD estimators…

统计方法学 · 统计学 2026-01-27 Ian Laga , Benjamin Vogel , Jieyun Wang , Anna Smith , Owen Ward

Social network data is often prohibitively expensive to collect, limiting empirical network research. Typical economic network mapping requires (1) enumerating a census, (2) eliciting the names of all network links for each individual, (3)…

统计方法学 · 统计学 2018-08-03 Emily Breza , Arun G. Chandrasekhar , Tyler H. McCormick , Mengjie Pan

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially groups most at-risk for HIV/AIDS. Data are collected through a peer-referral process in which current sample members…

统计方法学 · 统计学 2012-09-28 Krista J. Gile , Lisa G. Johnston , Matthew J. Salganik

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. While RDS is most frequently applied to estimate the prevalence of infections…

统计方法学 · 统计学 2016-10-24 Ashton M. Verdery , Jacob C. Fisher , Nalyn Siripong , Kahina Abdesselam , Shawn Bauldry

Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture-recapture and multiplier methods have become standard tools for inference of hidden…

统计方法学 · 统计学 2015-05-01 Forrest W. Crawford , Jiacheng Wu , Robert Heimer

Key populations at high risk of HIV infection are critical for understanding and monitoring HIV epidemics, but global estimation is hampered by sparse, uneven data. We analyze data from 199 countries for female sex workers (FSW), men who…

应用统计 · 统计学 2025-09-16 Jiahao Zhang , Keith Sabin , Le Bao

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. We developed a…

Ending the HIV/AIDS pandemic is among the Sustainable Development Goals for the next decade. In order to overcome the gap between the need for care and the available resources, better understanding of HIV epidemics is needed to guide policy…

应用统计 · 统计学 2020-09-03 Zhou Lan , Le Bao

Respondent-Driven Sampling (RDS) employs a variant of a link-tracing network sampling strategy to collect data from hard-to-reach populations. By tracing the links in the underlying social network, the process exploits the social structure…

应用统计 · 统计学 2009-04-14 Krista J. Gile , Mark S. Handcock

Network sampling is used around the world for surveys of vulnerable, hard-to-reach populations including people at risk for HIV, opioid misuse, and emerging epidemics. The sampling methods include tracing social links to add new people to…

统计方法学 · 统计学 2020-02-05 Steve Thompson
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