Bayesian Error-in-variables Models

Bayesian Error-In-Variables Models is a statistical modeling framework that belongs to the class of Bayesian regression models and specifically accounts for measurement errors in both dependent and independent variables by incorporating prior distributions on latent true values to correct estimation bias.

51

Publications

3.8K

Citations

114

Authors

83

Institutions

Publications per year

2017–2026

5

Authors

114

  1. PM
    Park M. Reilly

    University of Waterloo, Canada

    4 publications · 292 citations

    4

    h-index

  2. HB
    Heleno Bolfarine

    Universidade de São Paulo, Brazil

    3 publications · 44 citations

    3

    h-index

  3. SZ
    Shijie Zhou

    CSIRO Oceans and Atmosphere, Australia

    2 publications · 174 citations

    2

    h-index

  4. JA
    Jorge Alberto Achcar

    Universidade de São Paulo, Brazil

    2 publications · 31 citations

    2

    h-index

  5. AZ
    Arnold Zellner

    University of Chicago, United States

    2 publications · 111 citations

    2

    h-index

1–5 of 114

Venues

  1. Statistics in Medicine

    3 publications · 59 citations

    3

    h-index

  2. Journal of Econometrics

    3 publications · 256 citations

    3

    h-index

  3. Technometrics

    3 publications · 499 citations

    3

    h-index

  4. Journal of Multivariate Analysis

    3 publications · 76 citations

    3

    h-index

  5. The Canadian Journal of Chemical Engineering

    2 publications · 36 citations

    2

    h-index

1–5