Sparse Sampling Model Development

Sparse Sampling Model Development is a model development methodology in which the construction and training of predictive or analytical models are based on intentionally limited and non-dense data samples, distinguishing it from conventional approaches by prioritizing efficiency and representativeness under sparse data acquisition conditions.

17

Publications

644

Citations

66

Authors

30

Institutions

Publications per year

2017–2026

2

Authors

66

  1. AF
    Alan Forrest

    University at Buffalo, State University of New York, United States

    2 publications · 37 citations

    2

    h-index

  2. MW
    Mark W. Maciejewski

    UConn Health, United States

    1 publications · 53 citations

    1

    h-index

  3. YS
    Yongqiao Sun

    China University of Mining and Technology, China

    1 publications · 65 citations

    1

    h-index

  4. MM
    Milind M. Buddhikot

    Alcatel Lucent (Germany), Germany

    1 publications · 14 citations

    1

    h-index

  5. ZC
    Zhen Cai

    China University of Mining and Technology, China

    1 publications · 65 citations

    1

    h-index

1–5 of 66

Venues

  1. Journal of Magnetic Resonance

    JMR · 2 publications · 82 citations

    2

    h-index

  2. British Journal of Clinical Pharmacology

    BJCP · 2 publications · 162 citations

    2

    h-index

  3. Statistics Surveys

    1 publications · 57 citations

    1

    h-index

  4. IEEE Transactions on Signal Processing

    1 publications · 47 citations

    1

    h-index

  5. Environmental Monitoring and Assessment

    1 publications · 14 citations

    1

    h-index

1–5