Feature Screening

Feature Screening is a parametric performance analysis method that identifies and selects the most influential input variables by evaluating their individual contributions to output variability, thereby distinguishing itself from other analysis techniques through its focus on pre-modeling dimensionality reduction via univariate assessment.

153

Publications

10.2K

Citations

471

Authors

247

Institutions

Publications per year

2017–2026

100

Authors

471

  1. RL
    Runze Li

    Pennsylvania State University, United States

    11 publications · 2K citations

    11

    h-index

  2. YX
    Yitian Xu

    China Agricultural University, China

    4 publications · 63 citations

    4

    h-index

  3. JR
    Jialiang Ren

    Shanxi Medical University, China

    3 publications · 66 citations

    3

    h-index

  4. CX
    Chen Xu

    University of Ottawa, Canada

    3 publications · 133 citations

    3

    h-index

  5. PL
    Peng Lai

    Nanjing University of Information Science and Technology, China

    3 publications · 55 citations

    3

    h-index

1–5 of 471

Venues

  1. Journal of the American Statistical Association

    12 publications · 1.9K citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 181 citations

    6

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 5 publications · 97 citations

    5

    h-index

  4. Frontiers in Oncology

    5 publications · 179 citations

    5

    h-index

  5. The Annals of Statistics

    4 publications · 183 citations

    4

    h-index

1–5