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A Note on the Dependence Structure of Hierarchical Completely Random Measures

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Bayesian Statistics, New Generations New Approaches (BAYSM 2022)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 435))

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Abstract

Hierarchical models offer a principled framework to make inference and predictions on different (groups of) observations by leveraging their common features. In a nonparametric setting, the borrowing of information is controlled by the dependence structure induced on a vector of random measures. Two different hierarchical specifications are now well-established in the literature: we compare their dependence structures, provide some intuition on how to enhance their flexibility, and highlight a possibly misleading behaviour of their pairwise covariance. This note is based on some recent results in [3].

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Author information

Authors and Affiliations

  1. University of Warwick, Coventry, UK

    Marta Catalano

  2. Bocconi University, Milan, Italy

    Claudio Del Sole

Authors
  1. Marta Catalano
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  2. Claudio Del Sole
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Correspondence to Marta Catalano .

Editor information

Editors and Affiliations

  1. Institute of Statistics and Mathematical Methods in Economics, TU Wien, Vienna, Austria

    Alejandra Avalos-Pacheco

  2. Department of Biostatistics, Data Science Institute, Brown University, Providence, RI, USA

    Roberta De Vito

  3. Department of Mathematics and Statistics, Faculty of Arts and Science, University of Montreal, Montréal, QC, Canada

    Florian Maire

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Catalano, M., Del Sole, C. (2023). A Note on the Dependence Structure of Hierarchical Completely Random Measures. In: Avalos-Pacheco, A., De Vito, R., Maire, F. (eds) Bayesian Statistics, New Generations New Approaches. BAYSM 2022. Springer Proceedings in Mathematics & Statistics, vol 435. Springer, Cham. https://doi.org/10.1007/978-3-031-42413-7_8

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