Gaussian Error Models

Gaussian Error Models is a statistical modeling framework that assumes the residual unexplained variability in observed data follows a normal (Gaussian) distribution, distinguishing it from other error models by its reliance on the properties of Gaussian noise to quantify uncertainty and estimate parameters.

116

Publications

15.6K

Citations

272

Authors

168

Institutions

Publications per year

2017–2026

15

Variants

Gaussian Error ModelNormal Error ModelNormal Error Models

Authors

272

  1. ER
    Edward R. Dougherty

    Texas A&M University, United States

    3 publications · 107 citations

    3

    h-index

  2. JL
    Justin L. Tobias

    University of California, Irvine, United States

    2 publications · 286 citations

    2

    h-index

  3. WF
    Wolfgang Forstmeier

    Max Planck Institute for Ornithology, Germany

    2 publications · 696 citations

    2

    h-index

  4. EV
    Edward Vytlacil

    University of Chicago, United States

    3 publications · 332 citations

    2

    h-index

  5. RJ
    Runqing Jiang

    Grand River Hospital, Canada

    2 publications · 26 citations

    2

    h-index

1–5 of 272

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 4K citations

    4

    h-index

  2. Biometrika

    3 publications · 427 citations

    3

    h-index

  3. 2

    h-index

  4. The Astrophysical Journal

    ApJ · 2 publications · 228 citations

    2

    h-index

  5. Southern Economic Journal

    2 publications · 286 citations

    2

    h-index

1–5