Adaptive Cluster Sampling

Adaptive Cluster Sampling is a spatial sampling method in which the selection of additional sampling units is conditioned on the values observed at previously sampled locations, thereby allowing the sampling design to adaptively expand into clusters of units that meet a predefined criterion of interest.

62

Publications

2.9K

Citations

116

Authors

69

Institutions

Publications per year

2017–2026

4

Authors

116

  1. SK
    Steven K. Thompson

    Pennsylvania State University, United States

    8 publications · 1.1K citations

    8

    h-index

  2. AL
    Arthur L. Dryver

    National Institute of Development Administration, Thailand

    5 publications · 133 citations

    5

    h-index

  3. MS
    Mohammad Salehi

    Isfahan University of Technology, Iran

    4 publications · 136 citations

    4

    h-index

  4. GA
    George A. F. Seber

    University of Auckland, New Zealand

    4 publications · 218 citations

    4

    h-index

  5. MC
    Mary C. Christman

    University of Florida, United States

    3 publications · 149 citations

    3

    h-index

1–5 of 116

Venues

  1. Biometrics

    7 publications · 581 citations

    7

    h-index

  2. Environmetrics

    5 publications · 202 citations

    5

    h-index

  3. Biometrika

    4 publications · 251 citations

    4

    h-index

  4. 3

    h-index

  5. Environmental and Ecological Statistics

    5 publications · 111 citations

    3

    h-index

1–5