IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v138y2018icp27-30.html
   My bibliography  Save this article

A note on a network model with degree heterogeneity and homophily

Author

Listed:
  • Su, Liju
  • Qian, Xiaodi
  • Yan, Ting

Abstract

In this note, we establish a central limit theorem for the maximum likelihood estimator of the degree parameter in a network model with degree heterogeneity and homophily when the number of nodes goes to infinity.

Suggested Citation

  • Su, Liju & Qian, Xiaodi & Yan, Ting, 2018. "A note on a network model with degree heterogeneity and homophily," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 27-30.
    • Handle: RePEc:eee:stapro:v:138:y:2018:i:c:p:27-30
    DOI: 10.1016/j.spl.2018.02.046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715218300919
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2018.02.046?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Yan, Ting, 2015. "A note on asymptotic distributions in maximum entropy models for networks," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 1-5.
    2. Ting Yan & Jinfeng Xu, 2013. "A central limit theorem in the β-model for undirected random graphs with a diverging number of vertices," Biometrika, Biometrika Trust, vol. 100(2), pages 519-524.
    3. Bryan S. Graham, 2017. "An Econometric Model of Network Formation With Degree Heterogeneity," Econometrica, Econometric Society, vol. 85, pages 1033-1063, July.
    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. Jing Luo & Tour Liu & Qiuping Wang, 2022. "Affiliation weighted networks with a differentially private degree sequence," Statistical Papers, Springer, vol. 63(2), pages 367-395, April.
    2. Qiuping Wang & Yuan Zhang & Ting Yan, 2023. "Asymptotic theory in network models with covariates and a growing number of node parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 369-392, April.
    3. Long, Yuhang & Huang, Tao, 2022. "A note on a dynamic network model with homogeneous structure," Statistics & Probability Letters, Elsevier, vol. 184(C).

    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. Koen Jochmans & Martin Weidner, 2019. "Fixed‐Effect Regressions on Network Data," Econometrica, Econometric Society, vol. 87(5), pages 1543-1560, September.
    2. Qiuping Wang & Yuan Zhang & Ting Yan, 2023. "Asymptotic theory in network models with covariates and a growing number of node parameters," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 369-392, April.
    3. Ma, Shujie & Su, Liangjun & Zhang, Yichong, 2020. "Detecting Latent Communities in Network Formation Models," Economics and Statistics Working Papers 12-2020, Singapore Management University, School of Economics.
    4. Gao, Wayne Yuan & Li, Ming & Xu, Sheng, 2023. "Logical differencing in dyadic network formation models with nontransferable utilities," Journal of Econometrics, Elsevier, vol. 235(1), pages 302-324.
    5. Han, Ruijian & Chen, Kani & Tan, Chunxi, 2020. "Bivariate gamma model," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    6. Mingli Chen & Kengo Kato & Chenlei Leng, 2021. "Analysis of networks via the sparse β‐model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 887-910, November.
    7. Gao, Wayne Yuan, 2020. "Nonparametric identification in index models of link formation," Journal of Econometrics, Elsevier, vol. 215(2), pages 399-413.
    8. Yong, Zhang & Chen, Siyu & Qin, Hong & Yan, Ting, 2016. "Directed weighted random graphs with an increasing bi-degree sequence," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 235-240.
    9. Jing Luo & Tour Liu & Qiuping Wang, 2022. "Affiliation weighted networks with a differentially private degree sequence," Statistical Papers, Springer, vol. 63(2), pages 367-395, April.
    10. Lee, Jiyon, 2018. "A spatial latent class model," Economics Letters, Elsevier, vol. 162(C), pages 62-68.
    11. Chih‐Sheng Hsieh & Lung‐Fei Lee & Vincent Boucher, 2020. "Specification and estimation of network formation and network interaction models with the exponential probability distribution," Quantitative Economics, Econometric Society, vol. 11(4), pages 1349-1390, November.
    12. Bryan S. Graham, 2017. "An econometric model of network formation with degree heterogeneity," CeMMAP working papers 08/17, Institute for Fiscal Studies.
    13. Geert Ridder & Shuyang Sheng, 2020. "Two-Step Estimation of a Strategic Network Formation Model with Clustering," Papers 2001.03838, arXiv.org, revised Nov 2022.
    14. Áureo de Paula, 2015. "Econometrics of network models," CeMMAP working papers CWP52/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    15. Chen, Mingli & Fernández-Val, Iván & Weidner, Martin, 2021. "Nonlinear factor models for network and panel data," Journal of Econometrics, Elsevier, vol. 220(2), pages 296-324.
    16. Cristiano Varin & Manuela Cattelan & David Firth, 2016. "Statistical modelling of citation exchange between statistics journals," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 1-63, January.
    17. Monica Billio & Roberto Casarin & Matteo Iacopini, 2024. "Bayesian Markov-Switching Tensor Regression for Time-Varying Networks," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 109-121, January.
    18. Weidner, Martin & Zylkin, Thomas, 2021. "Bias and consistency in three-way gravity models," Journal of International Economics, Elsevier, vol. 132(C).
    19. Michael P. Leung, 2019. "Inference in Models of Discrete Choice with Social Interactions Using Network Data," Papers 1911.07106, arXiv.org.
    20. Ida Johnsson & Hyungsik Roger Moon, 2017. "Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach," Papers 1709.10024, arXiv.org, revised Jul 2019.

    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:eee:stapro:v:138:y:2018:i:c:p:27-30. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.