Optimal Sampling Stopping

Optimal Sampling Stopping is a sequential decision-theoretic framework within optimal stopping theory that prescribes the stopping rule maximizing expected utility or minimizing expected cost when sampling from a sequence of observations under uncertainty, distinguished by its integration of sampling costs and information gain in determining the optimal termination point.

37

Publications

2.3K

Citations

96

Authors

65

Institutions

Publications per year

2017–2026

4

Authors

96

  1. OP
    Odile Pons

    Institut National de la Recherche Agronomique, Morocco

    2 publications · 243 citations

    2

    h-index

  2. ML
    Melinda L. Moir

    The University of Melbourne, Australia

    1 publications · 35 citations

    1

    h-index

  3. TK
    Tugrul Kaplan

    Istinye University, Türkiye

    1 publications · 14 citations

    1

    h-index

  4. MM
    M. M. Dodson

    University of York, United Kingdom

    1 publications · 12 citations

    1

    h-index

  5. JW
    Jong‐Wuu Wu

    Tamkang University, Taiwan

    1 publications · 53 citations

    1

    h-index

1–5 of 96

Venues

  1. The Annals of Statistics

    4 publications · 238 citations

    4

    h-index

  2. The Annals of Mathematical Statistics

    3 publications · 70 citations

    3

    h-index

  3. Constructive Approximation

    2 publications · 90 citations

    2

    h-index

  4. Behavioural Processes

    1 publications · 44 citations

    1

    h-index

  5. 1

    h-index

1–5