Variance Component Identifiability

Variance Component Identifiability is a statistical property that determines whether the distinct sources of variability in a hierarchical or mixed-effects model can be uniquely estimated from the observed data, ensuring that different parameter values yield distinguishable joint distributions of the observations.

11

Publications

3.7K

Citations

28

Authors

20

Institutions

Publications per year

2017–2026

2

Variants

Variance Components Identifiability

Authors

28

  1. EE
    Eleazar Eskin

    University of California, Los Angeles, United States

    1 publications · 3.1K citations

    1

    h-index

  2. PS
    Paola Sebastiani

    Boston University, United States

    1 publications · 21 citations

    1

    h-index

  3. RJ
    R. J. C. Cantet

    Universidad de Buenos Aires, Argentina

    1 publications · 34 citations

    1

    h-index

  4. HM
    Hyun Min Kang

    University of Michigan, United States

    1 publications · 3.1K citations

    1

    h-index

  5. CM
    Cibele M. Russo

    Universidade de São Paulo, Brazil

    1 publications · 13 citations

    1

    h-index

1–5 of 28

Venues

  1. Journal of the Korean Statistical Society

    1 publications · 15 citations

    1

    h-index

  2. Test

    1 publications · 13 citations

    1

    h-index

  3. Journal of Animal Breeding and Genetics

    1 publications · 34 citations

    1

    h-index

  4. Academic Radiology

    1 publications · 75 citations

    1

    h-index

  5. Statistics & Probability Letters

    1 publications · 10 citations

    1

    h-index

1–5