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  1. Graphical model inference with external network data
    Autor*in: Jewson, Jack; Li, Li; Battaglia, Laura; Hansen, Stephen; Rossell, David; Zwiernik, Piotr
    Erschienen: 02 November 2022
    Verlag:  Centre for Economic Policy Research, London

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    Verlag (lizenzpflichtig)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    LZ 161
    Fernleihe:
    keine Fernleihe

    Standort:
    Universitätsbibliothek Mannheim
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: Array ; DP17638
    Schlagworte: Deskriptive Statistik; Bayes-Statistik; Induktive Statistik; Schätztheorie; Coronavirus; Geographische Entfernung; Social Web; Kapitalmarktrendite; USA; GLASSO; Bayesian Inference; Spike-and-Slab
    Umfang: 1 Online-Ressource (circa 59 Seiten), Illustrationen
  2. Graphical model inference with external network data
    Autor*in: Jewson, Jack; Li, Li; Battaglia, Laura; Hansen, Stephen; Rossell, David; Zwiernik, Piotr
    Erschienen: [2022]
    Verlag:  Cemmap, Centre for Microdata Methods and Practice, The Institute for Fiscal Studies, Department of Economics, UCL, [London]

    A frequent challenge when using graphical models in applications is that the sample size is limited relative to the number of parameters to be learned. Our motivation stems from applications where one has external data, in the form of networks... mehr

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    Resolving-System (kostenfrei)

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    DS 243
    Fernleihe:
    keine Fernleihe

     

    A frequent challenge when using graphical models in applications is that the sample size is limited relative to the number of parameters to be learned. Our motivation stems from applications where one has external data, in the form of networks between variables, that provides valuable information to help improve inference. Specifically, we depict the relation between COVID-19 cases and social and geographical network data, and between stock market returns and economic and policy networks extracted from text data. We propose a graphical LASSO framework where likelihood penalties are guided by the external network data. We also propose a spike-and-slab prior framework that depicts how partial correlations depend on the networks, which helps interpret the fitted graphical model and its relationship to the network. We develop computational schemes and software implementations in R and probabilistic programming languages. Our applications show how incorporating network data can significantly improve interpretation, statistical accuracy, and out-of-sample prediction, in some instances using significantly sparser graphical models than would have otherwise been estimated.

     
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/272832
    Schriftenreihe: Cemmap working paper ; CWP22, 20
    Schlagworte: Deskriptive Statistik; Bayes-Statistik; Induktive Statistik; Schätztheorie; Coronavirus; Geographische Entfernung; Social Web; Kapitalmarktrendite; USA; GLASSO; Bayesian Inference; Spike-and-Slab
    Umfang: 1 Online-Ressource (circa 58 Seiten), Illustrationen