IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v87y2015icp57-72.html
   My bibliography  Save this article

Faithfulness and learning hypergraphs from discrete distributions

Author

Listed:
  • Klimova, Anna
  • Uhler, Caroline
  • Rudas, Tamás

Abstract

The concepts of faithfulness and strong-faithfulness are important for statistical learning of graphical models. Graphs are not sufficient for describing the association structure of a discrete distribution. Hypergraphs representing hierarchical log-linear models are considered instead, and the concept of parametric (strong-)faithfulness with respect to a hypergraph is introduced. The strength of association in a discrete distribution can be quantified with various measures, leading to different concepts of strong-faithfulness. It is proven that strong-faithfulness defined in terms of interaction parameters ensures the existence of uniformly consistent parameter estimators and enables building uniformly consistent procedures for a hypergraph search. Lower and upper bounds for the proportions of distributions that do not satisfy strong-faithfulness are computed for different parameterizations and measures of association.

Suggested Citation

  • Klimova, Anna & Uhler, Caroline & Rudas, Tamás, 2015. "Faithfulness and learning hypergraphs from discrete distributions," Computational Statistics & Data Analysis, Elsevier, vol. 87(C), pages 57-72.
  • Handle: RePEc:eee:csdana:v:87:y:2015:i:c:p:57-72
    DOI: 10.1016/j.csda.2015.01.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315000286
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2015.01.017?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. David Edwards, 2012. "A note on adding and deleting edges in hierarchical log-linear models," Computational Statistics, Springer, vol. 27(4), pages 799-803, December.
    2. James M. Robins, 2003. "Uniform consistency in causal inference," Biometrika, Biometrika Trust, vol. 90(3), pages 491-515, September.
    3. Peter Spirtes & Clark Glymour & Richard Scheines, 2001. "Causation, Prediction, and Search, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262194406, April.
    Full references (including those not matched with items on IDEAS)

    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. Bareinboim Elias & Pearl Judea, 2013. "A General Algorithm for Deciding Transportability of Experimental Results," Journal of Causal Inference, De Gruyter, vol. 1(1), pages 107-134, June.
    2. Chen, Pu & Hsiao, Chih-Ying, 2010. "Looking behind Granger causality," MPRA Paper 24859, University Library of Munich, Germany.
    3. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    4. Maarten J. Bijlsma & Rhian M. Daniel & Fanny Janssen & Bianca L. De Stavola, 2017. "An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models," Demography, Springer;Population Association of America (PAA), vol. 54(2), pages 721-743, April.
    5. Chen, Pu & Hsiao, Chih-Ying, 2008. "What happens to Japan if China catches a cold?: A causal analysis of Chinese growth and Japanese growth," Japan and the World Economy, Elsevier, vol. 20(4), pages 622-638, December.
    6. Chen, Pu & Chihying, Hsiao, 2007. "Learning Causal Relations in Multivariate Time Series Data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 1, pages 1-43.
    7. Lima, Elcyon Caiado & Maka, Alexis & Céspedes, Brisne, 2008. "Monetary Policy, Inflation and the Level of Economic Activity in Brazil After the Real Plan: Stylized Facts from SVAR Models," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 62(2), October.
    8. Kaiyue Liu & Lihua Liu & Kaiming Xiao & Xuan Li & Hang Zhang & Yun Zhou & Hongbin Huang, 2024. "CL-NOTEARS: Continuous Optimization Algorithm Based on Curriculum Learning Framework," Mathematics, MDPI, vol. 12(17), pages 1-22, August.
    9. Mendonça, Mário Jorge & Loureiro, Paulo R.A. & Sachsida, Adolfo, 2012. "The dynamics of land-use in Brazilian Amazon," Ecological Economics, Elsevier, vol. 84(C), pages 23-36.
    10. Ziyu Wang & Yucen Luo & Yueru Li & Jun Zhu & Bernhard Scholkopf, 2022. "Spectral Representation Learning for Conditional Moment Models," Papers 2210.16525, arXiv.org, revised Dec 2022.
    11. Ruijie Tang, 2024. "Trading with Time Series Causal Discovery: An Empirical Study," Papers 2408.15846, arXiv.org, revised Aug 2024.
    12. Benjamin A Logsdon & Jason Mezey, 2010. "Gene Expression Network Reconstruction by Convex Feature Selection when Incorporating Genetic Perturbations," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-13, December.
    13. Stimel Derek, 2009. "A Statistical Analysis of NFL Quarterback Rating Variables," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(2), pages 1-26, May.
    14. Xingyu Liao & Xiaoping Liu, 2024. "Hidden Variable Discovery Based on Regression and Entropy," Mathematics, MDPI, vol. 12(9), pages 1-16, April.
    15. Ronja Foraita & Juliane Friemel & Kathrin Günther & Thomas Behrens & Jörn Bullerdiek & Rolf Nimzyk & Wolfgang Ahrens & Vanessa Didelez, 2020. "Causal discovery of gene regulation with incomplete data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1747-1775, October.
    16. Djordjilović, Vera & Chiogna, Monica, 2022. "Searching for a source of difference in graphical models," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    17. Behnam Azhdari & Jean Bonnet & Sébastien Bourdin, 2022. "Towards a Causal Model and Causal Inference of Regional Entrepreneurship Development Index, its antecedents and outcomes in European regions," Economics Working Paper Archive (University of Rennes & University of Caen) 2022-06, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    18. C Schultheiss & P Bühlmann, 2023. "Ancestor regression in linear structural equation models," Biometrika, Biometrika Trust, vol. 110(4), pages 1117-1124.
    19. Thomas S. Richardson & James M. Robins & Linbo Wang, 2018. "Discussion of “Data†driven confounder selection via Markov and Bayesian networks†by Häggström," Biometrics, The International Biometric Society, vol. 74(2), pages 403-406, June.
    20. Chen, Pu, 2010. "A time series causal model," MPRA Paper 24841, University Library of Munich, Germany.

    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:csdana:v:87:y:2015:i:c:p:57-72. 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/locate/csda .

    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.