Robust Priors

Robust Priors is a methodological framework in Bayesian statistics that integrates prior distributions designed to maintain inferential stability and validity across a wide range of model specifications and data conditions, thereby distinguishing it from conventional prior specifications sensitive to assumed parametric forms or outlying observations.

56

Publications

3.3K

Citations

108

Authors

75

Institutions

Publications per year

2017–2026

9

Variants

Robust Prior Distributions

Authors

108

  1. FS
    Frank Schorfheide

    University of Pennsylvania, United States

    3 publications · 417 citations

    3

    h-index

  2. MD
    Marco Del Negro

    Federal Reserve Bank of New York, United States

    3 publications · 417 citations

    3

    h-index

  3. LR
    Luis R. Pericchi

    Simón Bolívar University, Venezuela

    2 publications · 69 citations

    2

    h-index

  4. GC
    Guido Consonni

    University of Pavia, Italy

    2 publications · 38 citations

    2

    h-index

  5. LW
    Larry Wasserman

    Carnegie Mellon University, United States

    2 publications · 263 citations

    2

    h-index

1–5 of 108

Venues

  1. Bayesian Analysis

    6 publications · 219 citations

    6

    h-index

  2. The Annals of Statistics

    5 publications · 589 citations

    5

    h-index

  3. Journal of Multivariate Analysis

    3 publications · 57 citations

    3

    h-index

  4. 3

    h-index

  5. Econometrica

    3 publications · 171 citations

    3

    h-index

1–5