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H 1 Centered partition processes: lumping versus splitting in sparse health data

Auteurs : Herring, Amy (Auteur de la Conférence)
CIRM (Editeur )

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    Résumé : In many health studies, interest often lies in assessing health effects on a large set of outcomes or specific outcome subtypes, which may be sparsely observed, even in big data settings. For example, while the overall prevalence of birth defects is not low, the vast heterogeneity in types of congenital malformations leads to challenges in estimation for sparse groups. However, lumping small groups together to facilitate estimation is often controversial and may have limited scientific support.
    There is a very rich literature proposing Bayesian approaches for clustering starting with a prior probability distribution on partitions. Most approaches assume exchangeability, leading to simple representations in terms of Exchangeable Partition Probability Functions (EPPF). Gibbs-type priors encompass a broad class of such cases, including Dirichlet and Pitman-Yor processes. Even though there have been some proposals to relax the exchangeability assumption, allowing covariate-dependence and partial exchangeability, limited consideration has been given on how to include concrete prior knowledge on the partition. We wish to cluster birth defects into groups to facilitate estimation, and we have prior knowledge of an initial clustering provided by experts. As a general approach for including such prior knowledge, we propose a Centered Partition (CP) process that modifies the EPPF to favor partitions close to an initial one. Some properties of the CP prior are described, a general algorithm for posterior computation is developed, and we illustrate the methodology through simulation examples and an application to the motivating epidemiology study of birth defects.

    Keywords : Bayesian nonparametrics; exchangeable partition probability functions; exchangeable Gibbs partitions; Gibbs-type priors; estimation; centered partition; Dirichlet process

    Codes MSC :
    60G09 - Exchangeability
    60G57 - Random measures
    62F15 - Bayesian inference
    62G05 - Nonparametric estimation
    62H30 - Classification and discrimination; cluster analysis
    62P10 - Applications of statistics to biology and medical sciences

      Informations sur la Vidéo

      Langue : Anglais
      Date de publication : 06/12/2018
      Date de captation : 26/11/2018
      Collection : Research talks
      Format : MP4
      Durée : 00:55:14
      Domaine : Probability & Statistics
      Audience : Chercheurs ; Doctorants , Post - Doctorants
      Download : https://videos.cirm-math.fr/2018-11-26_Herring.mp4

    Informations sur la rencontre

    Nom de la rencontre : Jean-Morlet chair: Bayesian statistics in the big data era / Chaire Jean-Morlet : Statistiques bayésiennes à l'ère du big data
    Organisateurs de la rencontre : Freyemurth, Jean-Marc ; Marin, Jean-Michel ; Mengersen, Kerrie ; Pommeret, Denys ; Pudlo, Pierre
    Dates : 26/11/2018 - 30/11/2018
    Année de la rencontre : 2018
    URL Congrès : https://www.chairejeanmorlet.com/2018-2-...

    Citation Data

    DOI : 10.24350/CIRM.V.19477903
    Cite this video as: Herring, Amy (2018). Centered partition processes: lumping versus splitting in sparse health data. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19477903
    URI : http://dx.doi.org/10.24350/CIRM.V.19477903


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