Adapted Stopping Rules

Adapted Stopping Rules is a stochastic decision rule in the theory of stochastic processes that specifies a stopping time based solely on the information available up to each moment, thereby being measurable with respect to the underlying filtration.

9

Publications

742

Citations

21

Authors

20

Institutions

Publications per year

2017–2026

0

Authors

21

  1. AM
    Annie Millet

    Laboratoire de Probabilités et Modèles Aléatoires, France

    2 publications · 39 citations

    2

    h-index

  2. RD
    Roxana Dumitrescu

    King's College London, United Kingdom

    1 publications · 43 citations

    1

    h-index

  3. LS
    Louis Sucheston

    The Ohio State University, United States

    1 publications · 27 citations

    1

    h-index

  4. CS
    Christian Schmidt

    INSEAD, France

    1 publications · 257 citations

    1

    h-index

  5. MJ
    Monique Jeanblanc

    Université d'Évry Val-d'Essonne, France

    1 publications · 177 citations

    1

    h-index

1–5 of 21

Venues

  1. Statistics in Medicine

    1 publications · 113 citations

    1

    h-index

  2. The Annals of Probability

    1 publications · 12 citations

    1

    h-index

  3. Management Science

    1 publications · 257 citations

    1

    h-index

  4. Electronic Journal of Probability

    EJP · 1 publications · 43 citations

    1

    h-index

  5. Canadian Journal of Mathematics

    1 publications · 27 citations

    1

    h-index

1–5