Bayesian Prediction Intervals

Bayesian Prediction Intervals is a statistical interval estimate that belongs to the class of Bayesian inference methods, distinguished by its quantification of uncertainty in predicting future observations or effect sizes in meta-analysis through the posterior predictive distribution, incorporating both within-study variability and between-study heterogeneity.

110

Publications

4.5K

Citations

237

Authors

162

Institutions

Publications per year

2017–2026

22

Authors

237

  1. EK
    Essam K. AL-Hussaini

    Assiut University, Egypt

    7 publications · 220 citations

    7

    h-index

  2. AR
    A. R. Shafay

    Fayoum University, Egypt

    5 publications · 137 citations

    5

    h-index

  3. ZF
    Zeinhum F. Jaheen

    Assiut University, Egypt

    5 publications · 97 citations

    5

    h-index

  4. MM
    M. M. Mohie El-Din

    Al-Azhar University, Egypt

    4 publications · 90 citations

    4

    h-index

  5. NB
    N. Balakrishnan

    McMaster University, Canada

    4 publications · 99 citations

    4

    h-index

1–5 of 237

Venues

  1. Statistics

    7 publications · 155 citations

    7

    h-index

  2. Technometrics

    7 publications · 283 citations

    7

    h-index

  3. Journal of Statistical Computation and Simulation

    JSCS · 7 publications · 164 citations

    7

    h-index

  4. 6

    h-index

  5. Test

    3 publications · 101 citations

    3

    h-index

1–5