Adaptive Sampling Design

Adaptive Sampling Design is a sampling strategy in which the selection of subsequent observation units is sequentially determined by data-dependent rules based on information collected during the survey, thereby distinguishing it from non-adaptive designs that pre-specify all sampling decisions prior to data collection.

176

Publications

7.3K

Citations

523

Authors

273

Institutions

Publications per year

2017–2026

56

Authors

523

  1. SK
    Steven K. Thompson

    Pennsylvania State University, United States

    7 publications · 412 citations

    7

    h-index

  2. GA
    George A. F. Seber

    University of Auckland, New Zealand

    5 publications · 213 citations

    5

    h-index

  3. BS
    Barry Schouten

    Centraal Bureau voor de Statistiek, Netherlands

    4 publications · 186 citations

    4

    h-index

  4. DR
    David R. Smith

    United States Geological Survey, United States

    4 publications · 106 citations

    4

    h-index

  5. MS
    Mohammad Salehi

    Isfahan University of Technology, Iran

    4 publications · 102 citations

    4

    h-index

1–5 of 523

Venues

  1. Environmetrics

    5 publications · 169 citations

    5

    h-index

  2. IEEE Transactions on Automatic Control

    5 publications · 164 citations

    5

    h-index

  3. IEEE Access

    4 publications · 101 citations

    4

    h-index

  4. Biometrics

    4 publications · 274 citations

    4

    h-index

  5. Biometrika

    4 publications · 251 citations

    4

    h-index

1–5