Robust Data Preprocessing

Robust Data Preprocessing is a data preparation methodology that belongs to the class of outlier treatment techniques and is distinguished by its capacity to minimize the influence of outliers and noise on downstream analysis through stable, outlier-resistant transformations without prior assumptions about the underlying data distribution.

125

Publications

18.5K

Citations

448

Authors

236

Institutions

Publications per year

2017–2026

53

Authors

448

  1. DC
    David C. Hoaglin

    Harvard University, United States

    4 publications · 1.8K citations

    4

    h-index

  2. JW
    John W. Tukey

    Princeton University, United States

    4 publications · 1.8K citations

    4

    h-index

  3. FM
    Frederick Mosteller

    Harvard University Press, United States

    4 publications · 1.8K citations

    4

    h-index

  4. MH
    Mia Hubert

    KU Leuven, Belgium

    3 publications · 197 citations

    3

    h-index

  5. MW
    Ming Wan

    Stony Brook University, United States

    2 publications · 81 citations

    2

    h-index

1–5 of 448

Venues

  1. Analytical Chemistry

    3 publications · 324 citations

    3

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 3 publications · 316 citations

    3

    h-index

  3. Technometrics

    2 publications · 317 citations

    2

    h-index

  4. Biometrics

    2 publications · 242 citations

    2

    h-index

  5. Psychological Bulletin

    2 publications · 2.3K citations

    2

    h-index

1–5