Model-based Resampling

Model-Based Resampling is a resampling technique that generates new samples from an estimated parametric or semi-parametric model of the data distribution, as opposed to relying solely on empirical data repetition or permutation.

36

Publications

4.4K

Citations

90

Authors

64

Institutions

Publications per year

2017–2026

8

Authors

90

  1. YP
    Yogendra P. Chaubey

    Concordia University, Canada

    3 publications · 1.4K citations

    3

    h-index

  2. WL
    Wei‐Yin Loh

    University of Wisconsin–Madison, United States

    2 publications · 83 citations

    2

    h-index

  3. XZ
    Xiaodong Zheng

    University of Science and Technology of China, China

    2 publications · 83 citations

    2

    h-index

  4. PI
    Phillip I. Good

    W.E. Upjohn Institute for Employment Research, United States

    2 publications · 514 citations

    2

    h-index

  5. YJ
    Yongho Jeon

    Yonsei University, South Korea

    1 publications · 29 citations

    1

    h-index

1–5 of 90

Venues

  1. 5

    h-index

  2. Technometrics

    4 publications · 1.5K citations

    4

    h-index

  3. International Journal of Data Science and Analytics

    JDSA · 1 publications · 64 citations

    1

    h-index

  4. Journal of Neuroscience Methods

    1 publications · 30 citations

    1

    h-index

  5. Analytical Chemistry

    1 publications · 36 citations

    1

    h-index

1–5