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Random walks on directed networks: Inference and respondent-driven sampling
Stockholm University, Faculty of Science, Department of Mathematics.
2016 (English)In: Journal of Official Statistics, ISSN 0282-423X, E-ISSN 2001-7367, Vol. 32, no 2, 433-459 p.Article in journal (Refereed) Published
Abstract [en]

Respondent-driven sampling (RDS) is often used to estimate population properties (e.g., sexual risk behavior) in hard-to-reach populations. In RDS, already sampled individuals recruit population members to the sample from their social contacts in an efficient snowball-like sampling procedure. By assuming a Markov model for the recruitment of individuals, asymptotically unbiased estimates of population characteristics can be obtained. Current RDS estimation methodology assumes that the social network is undirected, that is, all edges are reciprocal. However, empirical social networks in general also include a substantial number of nonreciprocal edges. In this article, we develop an estimation method for RDS in populations connected by social networks that include reciprocal and nonreciprocal edges. We derive estimators of the selection probabilities of individuals as a function of the number of outgoing edges of sampled individuals. The proposed estimators are evaluated on artificial and empirical networks and are shown to generally perform better than existing estimators. This is the case in particular when the fraction of directed edges in the network is large.

Place, publisher, year, edition, pages
2016. Vol. 32, no 2, 433-459 p.
Keyword [en]
Hidden population, social network, nonreciprocal relationship, Markov model
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:su:diva-129251DOI: 10.1515/JOS-2016-0023ISI: 000377566800011OAI: oai:DiVA.org:su-129251DiVA: diva2:920625
Funder
Swedish Research Council, 2009-5759Riksbankens Jubileumsfond, P12-0705:1
Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2016-07-04Bibliographically approved
In thesis
1. Studies in respondent-driven sampling: Directed networks, epidemics, and random walks
Open this publication in new window or tab >>Studies in respondent-driven sampling: Directed networks, epidemics, and random walks
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Respondent-driven sampling (RDS) is a link-tracing sampling methodology especially suitable for sampling hidden populations. A clever sampling mechanism and inferential procedures that facilitate asymptotically unbiased population estimates has contributed to the rising popularity of the method. The papers in this thesis extend RDS estimation theory to some population structures to which the classical RDS estimation framework is not applicable and analyse the behaviour of the RDS recruitment process. 

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2016. 46 p.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-129287 (URN)978-91-7649-430-1 (ISBN)
Public defence
2016-06-15, sal 14, hus 5, Kräftriket, Roslagsvägen 101, Stockholm, 10:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: In press. Paper 3: Accepted. Paper 4: Manuscript.

Available from: 2016-05-23 Created: 2016-04-20 Last updated: 2016-05-11Bibliographically approved
2. Some advances in Respondent-driven sampling on directed social networks
Open this publication in new window or tab >>Some advances in Respondent-driven sampling on directed social networks
2013 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Respondent-driven sampling (RDS) is one of the most commonly used methods when sampling from hidden or hard-to-reach populations. The RDS methodology combines an improved snowball sampling scheme with a mathematical model that is able to produce unbiased population estimates given that some assumptions about the actual recruitment process are fulfilled. One critical assumption, which is not likely to hold in most cases, is that the underlying social network of the population is undirected. The papers in this thesis provide extensions of RDS theory to populations with partially directed social networks.

Place, publisher, year, edition, pages
Stockholm: Department of Mathematics, Stockholm University, 2013. 80 p.
National Category
Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
urn:nbn:se:su:diva-94704 (URN)
Presentation
2013-11-01, Rum 306, Matematiska institutionen, Hus 6, Kräftriket, Stockholm, 10:00
Opponent
Supervisors
Funder
Swedish Research Council, 2009-5759
Available from: 2013-10-09 Created: 2013-10-09 Last updated: 2016-05-11Bibliographically approved

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