Autocorrelation Models

Autocorrelation Models is a statistical framework within longitudinal data analysis that characterizes the temporal dependency structure among repeated measurements by modeling the correlation of a variable with itself across different time points, distinguishing it from other covariance structures through its explicit specification of lag-based dependencies.

673

Publications

43.7K

Citations

1.7K

Authors

778

Institutions

Publications per year

2017–2026

109

Authors

1.7K

  1. BE
    Bradley E. Huitema

    Western Michigan University, United States

    6 publications · 538 citations

    6

    h-index

  2. JW
    Joseph W. McKean

    Western Michigan University, United States

    5 publications · 427 citations

    5

    h-index

  3. JD
    James Devillers

    Centre National de la Recherche Scientifique, France

    5 publications · 98 citations

    5

    h-index

  4. DD
    D. Domine

    Joint Research Centre, Italy

    4 publications · 87 citations

    4

    h-index

  5. MM
    Malcolm M. Dow

    Northwestern University, United States

    4 publications · 243 citations

    4

    h-index

1–5 of 1.7K

Venues

  1. The Journal of the Acoustical Society of America

    JASA · 15 publications · 558 citations

    13

    h-index

  2. 10

    h-index

  3. Biometrika

    10 publications · 1.1K citations

    10

    h-index

  4. SSRN Electronic Journal

    Repository · 9 publications · 341 citations

    9

    h-index

  5. 8

    h-index

1–5