Priors For Variable Selection

Priors for Variable Selection is a Bayesian statistical framework that specifies prior distributions over model parameters to facilitate the inclusion or exclusion of predictors in regression models by assigning differential probabilities to variable subsets according to their relevance.

173

Publications

20.6K

Citations

313

Authors

186

Institutions

Publications per year

2017–2026

32

Authors

313

  1. EI
    Edward I. George

    University of Pennsylvania, United States

    7 publications · 1.7K citations

    7

    h-index

  2. MF
    Mark F. J. Steel

    University of Warwick, United Kingdom

    7 publications · 870 citations

    7

    h-index

  3. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    7 publications · 280 citations

    7

    h-index

  4. EL
    Eduardo Ley

    International Monetary Fund, United States

    5 publications · 793 citations

    5

    h-index

  5. DB
    David B. Dunson

    Duke University, United States

    5 publications · 414 citations

    5

    h-index

1–5 of 313

Venues

  1. Journal of the American Statistical Association

    18 publications · 4.6K citations

    18

    h-index

  2. Bayesian Analysis

    11 publications · 671 citations

    11

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 12 publications · 401 citations

    11

    h-index

  4. The Annals of Statistics

    10 publications · 2.7K citations

    10

    h-index

  5. Journal of Computational and Graphical Statistics

    JCGS · 5 publications · 130 citations

    5

    h-index

1–5