Stochastic Approximation Expectation-maximization

Stochastic Approximation Expectation-Maximization is a likelihood-based parameter estimation algorithm within the class of expectation-maximization methods that incorporates stochastic approximations to handle complex population pharmacokinetic models with incomplete data by iteratively updating parameter estimates using Monte Carlo sampling and recursive stochastic approximation.

108

Publications

6.1K

Citations

277

Authors

191

Institutions

Publications per year

2017–2026

24

Authors

277

  1. FM
    France Mentré

    Inserm, France

    9 publications · 381 citations

    9

    h-index

  2. ML
    Marc Lavielle

    Université Paris-Sud, France

    7 publications · 1.3K citations

    7

    h-index

  3. AS
    Adeline Samson

    Université Paris Cité, France

    6 publications · 307 citations

    6

    h-index

  4. PS
    Payman Sadegh

    Technical University of Denmark, Denmark

    4 publications · 253 citations

    4

    h-index

  5. EK
    Estelle Kuhn

    Mathématiques et Informatique Appliquées du Génome à l'Environnement, France

    4 publications · 387 citations

    4

    h-index

1–5 of 277

Venues

  1. Statistics in Medicine

    5 publications · 226 citations

    5

    h-index

  2. Expert Systems with Applications

    3 publications · 106 citations

    3

    h-index

  3. Biometrics

    3 publications · 107 citations

    3

    h-index

  4. The Annals of Applied Statistics

    AOAS · 3 publications · 219 citations

    3

    h-index

  5. 3

    h-index

1–5