IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v66y2017i3p501-519.html
   My bibliography  Save this article

Analysis of networks with missing data with application to the National Longitudinal Study of Adolescent Health

Author

Listed:
  • Krista J. Gile
  • Mark S. Handcock

Abstract

No abstract is available for this item.

Suggested Citation

  • Krista J. Gile & Mark S. Handcock, 2017. "Analysis of networks with missing data with application to the National Longitudinal Study of Adolescent Health," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 501-519, April.
    • Handle: RePEc:bla:jorssc:v:66:y:2017:i:3:p:501-519
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssc.12184
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hunter, David R. & Goodreau, Steven M. & Handcock, Mark S., 2008. "Goodness of Fit of Social Network Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 248-258, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Siliang & Chen, Yunxiao, 2024. "A note on Ising network analysis with missing data," LSE Research Online Documents on Economics 123984, London School of Economics and Political Science, LSE Library.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sándor Juhász, 2021. "Spinoffs and tie formation in cluster knowledge networks," Small Business Economics, Springer, vol. 56(4), pages 1385-1404, April.
    2. Kayvan Sadeghi & Alessandro Rinaldo, 2020. "Hierarchical models for independence structures of networks," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 439-457, August.
    3. John McLevey & Alexander V. Graham & Reid McIlroy-Young & Pierson Browne & Kathryn S. Plaisance, 2018. "Interdisciplinarity and insularity in the diffusion of knowledge: an analysis of disciplinary boundaries between philosophy of science and the sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 331-349, October.
    4. Tom A. B. Snijders & Christian E. G. Steglich, 2015. "Representing Micro–Macro Linkages by Actor-based Dynamic Network Models," Sociological Methods & Research, , vol. 44(2), pages 222-271, May.
    5. Stefano Ghinoi & Riccardo Vita & Bodo Steiner & Alessandro Sinatra, 2024. "Family firm network strategies in regional clusters: evidence from Italy," Small Business Economics, Springer, vol. 62(1), pages 87-103, January.
    6. Cornelius Fritz & Michael Lebacher & Göran Kauermann, 2020. "Tempus volat, hora fugit: A survey of tie‐oriented dynamic network models in discrete and continuous time," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(3), pages 275-299, August.
    7. Li, Jing & Yu, Qian & Ma, Ding, 2024. "Does China's high-speed rail network promote inter-city technology transfer? ——A multilevel network analysis based on the electronic information industry," Transport Policy, Elsevier, vol. 145(C), pages 11-24.
    8. Tiandong Wang & Panpan Zhang, 2022. "Directed hybrid random networks mixing preferential attachment with uniform attachment mechanisms," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 957-986, October.
    9. Anna‐Maria Kindt & Matthias Geissler & Kilian Bühling, 2022. "Be my (little) partner?!—Universities' role in regional innovation systems when large firms are rare," Journal of Regional Science, Wiley Blackwell, vol. 62(5), pages 1274-1295, November.
    10. Milad Abbasiharofteh & Tom Broekel, 2021. "Still in the shadow of the wall? The case of the Berlin biotechnology cluster," Environment and Planning A, , vol. 53(1), pages 73-94, February.
    11. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    12. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    13. Kabirigi, Michel & Abbasiharofteh, Milad & Sun, Zhanli & Hermans, Frans, 2022. "The importance of proximity dimensions in agricultural knowledge and innovation systems: The case of banana disease management in Rwanda," Agricultural Systems, Elsevier, vol. 202(C).
    14. Carter, Nathan & Hadlock, Charles & Haughton, Dominique, 2008. "Generating random networks from a given distribution," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 3928-3938, April.
    15. Tom Broekel & Marcel Bednarz, 2018. "Disentangling link formation and dissolution in spatial networks: An Application of a Two-Mode STERGM to a Project-Based R&D Network in the German Biotechnology Industry," Networks and Spatial Economics, Springer, vol. 18(3), pages 677-704, September.
    16. Clark, Duncan A. & Macinko, James & Porfiri, Maurizio, 2022. "What factors drive state firearm law adoption? An application of exponential-family random graph models," Social Science & Medicine, Elsevier, vol. 305(C).
    17. Hsieh, Chih-Sheng & König, Michael D. & Liu, Xiaodong & Zimmermann, Christian, 2018. "Superstar Economists: Coauthorship Networks and Research Output," IZA Discussion Papers 11916, Institute of Labor Economics (IZA).
    18. Martin Rosvall & Carl T Bergstrom, 2010. "Mapping Change in Large Networks," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-7, January.
    19. Duncan A. Clark & Mark S. Handcock, 2022. "Comparing the real‐world performance of exponential‐family random graph models and latent order logistic models for social network analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 566-587, April.
    20. SOHN Christophe & CHRISTOPOULOS Dimitris & KOSKINEN Johan, 2013. "Geography and social networks. Modelling the effects of territorial borders on policy networks," LISER Working Paper Series 2013-19, Luxembourg Institute of Socio-Economic Research (LISER).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:66:y:2017:i:3:p:501-519. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.