Data Smoothing

Data Smoothing is a data transformation technique that reduces random variation in a dataset by applying a mathematical function to approximate underlying patterns while attenuating noise.

507

Publications

38K

Citations

1.1K

Authors

523

Institutions

Publications per year

2017–2026

72

Authors

1.1K

  1. GW
    Grace Wahba

    University of Wisconsin–Madison, United States

    6 publications · 5.3K citations

    6

    h-index

  2. JS
    J. S. Marron

    University of North Carolina at Chapel Hill, United States

    5 publications · 292 citations

    5

    h-index

  3. ST
    Shang‐Hua Teng

    University of Southern California, United States

    5 publications · 1.3K citations

    5

    h-index

  4. DA
    Daniel A. Spielman

    Yale University, United States

    4 publications · 1.2K citations

    4

    h-index

  5. MP
    M. P. Wand

    UNSW Sydney, Australia

    4 publications · 657 citations

    4

    h-index

1–5 of 1.1K

Venues

  1. 19

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 9 publications · 1.2K citations

    9

    h-index

  3. Technometrics

    9 publications · 1.9K citations

    9

    h-index

  4. 7

    h-index

  5. Numerische Mathematik

    6 publications · 5.4K citations

    6

    h-index

1–5