Homoscedastic Error Models

Homoscedastic Error Models is a statistical modeling framework characterized by the assumption that the variance of the residual unexplained variability remains constant across all levels of the independent variables, distinguishing it from heteroscedastic models where error variance changes systematically with the predictors.

116

Publications

16.7K

Citations

242

Authors

153

Institutions

Publications per year

2017–2026

24

Authors

242

  1. JA
    James Algina

    University of Florida, United States

    3 publications · 241 citations

    3

    h-index

  2. SV
    Stef van Buuren

    Netherlands Organisation for Applied Scientific Research, Netherlands

    2 publications · 178 citations

    2

    h-index

  3. AH
    A. Hedayat

    University of Illinois Chicago, United States

    2 publications · 66 citations

    2

    h-index

  4. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    2 publications · 71 citations

    2

    h-index

  5. MR
    Matthieu Resche‐Rigon

    Inserm, France

    2 publications · 178 citations

    2

    h-index

1–5 of 242

Venues

  1. 6

    h-index

  2. Economics Letters

    4 publications · 4.8K citations

    4

    h-index

  3. The Annals of Statistics

    4 publications · 1.9K citations

    4

    h-index

  4. Biometrics

    3 publications · 290 citations

    3

    h-index

  5. Journal of Statistical Computation and Simulation

    JSCS · 3 publications · 41 citations

    3

    h-index

1–5