Marginal Mean Function

The Marginal Mean Function is a population-averaged stochastic process model that specifies the expected number of recurrent events experienced by an individual up to a given time, accounting for within-subject correlation without requiring full specification of the dependency structure.

40

Publications

4.9K

Citations

85

Authors

56

Institutions

Publications per year

2017–2026

6

Variants

Mean Cumulative Function

Authors

85

  1. DG
    Debashis Ghosh

    University of Michigan, United States

    2 publications · 436 citations

    2

    h-index

  2. JS
    John S. Preisser

    University of North Carolina at Chapel Hill, United States

    2 publications · 43 citations

    2

    h-index

  3. DE
    Douglas E. Schaubel

    University of Michigan, United States

    2 publications · 125 citations

    2

    h-index

  4. NL
    Nikolai Leonenko

    Cardiff University, United Kingdom

    2 publications · 108 citations

    2

    h-index

  5. CC
    C. C. Heyde

    Australian National University, Australia

    2 publications · 108 citations

    2

    h-index

1–5 of 85

Venues

  1. Statistics in Medicine

    3 publications · 121 citations

    3

    h-index

  2. Biometrics

    3 publications · 345 citations

    3

    h-index

  3. Lifetime Data Analysis

    2 publications · 125 citations

    2

    h-index

  4. Journal of Statistical Computation and Simulation

    JSCS · 2 publications · 64 citations

    2

    h-index

  5. The American Statistician

    2 publications · 2.4K citations

    2

    h-index

1–5