Nonparametric Hierarchical Models

Nonparametric Hierarchical Models is a statistical modeling framework that extends hierarchical models by allowing the number of parameters to adapt to data complexity through nonparametric Bayesian methods, thereby avoiding fixed parametric assumptions about the underlying distributional form.

80

Publications

3.9K

Citations

209

Authors

108

Institutions

Publications per year

2017–2026

13

Authors

209

  1. RA
    Raffaele Argiento

    Istituto di Matematica Applicata e Tecnologie Informatiche, Italy

    4 publications · 88 citations

    4

    h-index

  2. FA
    Fernando A. Quintana

    Pontificia Universidad Católica de Chile, Chile

    3 publications · 221 citations

    3

    h-index

  3. DB
    David B. Dunson

    Duke University, United States

    3 publications · 88 citations

    3

    h-index

  4. RK
    Robert Kohn

    UNSW Sydney, Australia

    3 publications · 139 citations

    3

    h-index

  5. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    3 publications · 134 citations

    3

    h-index

1–5 of 209

Venues

  1. Journal of the American Statistical Association

    11 publications · 895 citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 80 citations

    4

    h-index

  3. Biometrics

    3 publications · 63 citations

    3

    h-index

  4. Statistical Science

    2 publications · 80 citations

    2

    h-index

  5. Journal of Statistical Planning and Inference

    2 publications · 27 citations

    2

    h-index

1–5