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Modeling of networked populations when data is sampled or missing

Author

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  • Ian E. Fellows

    (Fellows Statistics)

  • Mark S. Handcock

    (University of California, Los Angeles)

Abstract

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.

Suggested Citation

  • Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    • Handle: RePEc:spr:metron:v:81:y:2023:i:1:d:10.1007_s40300-023-00246-3
    DOI: 10.1007/s40300-023-00246-3
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    References listed on IDEAS

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    1. Hoff P.D. & Raftery A.E. & Handcock M.S., 2002. "Latent Space Approaches to Social Network Analysis," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1090-1098, December.
    2. Mark S. Handcock & Krista J. Gile & Corinne M. Mar, 2015. "Estimating the size of populations at high risk for HIV using respondent-driven sampling data," Biometrics, The International Biometric Society, vol. 71(1), pages 258-266, March.
    3. Krista J. Gile & Mark S. Handcock, 2015. "Network model-assisted inference from respondent-driven sampling data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 619-639, June.
    4. Gile, Krista J., 2011. "Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 135-146.
    5. Neil M. Ferguson & Christl A. Donnelly & Roy M. Anderson, 2001. "Erratum: Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain," Nature, Nature, vol. 414(6861), pages 329-329, November.
    6. Neil M. Ferguson & Christl A. Donnelly & Roy M. Anderson, 2001. "Transmission intensity and impact of control policies on the foot and mouth epidemic in Great Britain," Nature, Nature, vol. 413(6855), pages 542-548, October.
    7. Don Klinkenberg & Christophe Fraser & Hans Heesterbeek, 2006. "The Effectiveness of Contact Tracing in Emerging Epidemics," PLOS ONE, Public Library of Science, vol. 1(1), pages 1-7, December.
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    1. M. Giovanna Ranalli & Jean-François Beaumont & Gaia Bertarelli & Natalie Shlomo, 2023. "Foreword to the special issue on “Survey Methods for Statistical Data Integration and New Data Sources”," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 1-3, April.

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