Bayesian Small Area Estimation

Bayesian Small Area Estimation is a statistical methodology within small area estimation that incorporates Bayesian inference to combine sample survey data with prior information or hierarchical models, thereby improving the precision of estimates for small domains through the explicit modeling of uncertainty and random effects.

82

Publications

4.7K

Citations

166

Authors

97

Institutions

Publications per year

2017–2026

17

Authors

166

  1. GS
    Gauri Sankar Datta

    University of Georgia, United States

    11 publications · 600 citations

    11

    h-index

  2. JN
    J. N. K. Rao

    Carleton University, Canada

    8 publications · 1K citations

    8

    h-index

  3. PL
    Partha Lahiri

    University of Maryland, College Park, United States

    6 publications · 354 citations

    6

    h-index

  4. BN
    Balgobin Nandram

    Worcester Polytechnic Institute, United States

    6 publications · 112 citations

    6

    h-index

  5. MG
    Malay Ghosh

    University of Florida, United States

    6 publications · 516 citations

    6

    h-index

1–5 of 166

Venues

  1. Journal of the American Statistical Association

    12 publications · 789 citations

    11

    h-index

  2. Statistical Science

    5 publications · 1.3K citations

    5

    h-index

  3. Journal of Statistical Planning and Inference

    5 publications · 253 citations

    5

    h-index

  4. 4

    h-index

  5. Journal of Official Statistics

    JOS · 4 publications · 64 citations

    4

    h-index

1–5