Box-cox Transformation

Box-Cox Transformation is a parametric data transformation method that belongs to the class of power transformations and is distinguished by its use of a tunable parameter λ to stabilize variance and make the data more closely approximate a normal distribution.

475

Publications

31.5K

Citations

1.3K

Authors

696

Institutions

Publications per year

2017–2026

94

Variants

Box-cox Power Transformation

Authors

1.3K

  1. JM
    Jeremy M. G. Taylor

    University of Michigan, United States

    5 publications · 222 citations

    5

    h-index

  2. RA
    Richard A. Johnson

    University of Wisconsin–Madison, United States

    5 publications · 428 citations

    5

    h-index

  3. MD
    M.G. Dagenais

    Université de Montréal, Canada

    5 publications · 230 citations

    5

    h-index

  4. KA
    Kjell A. Doksum

    University of California, Berkeley, United States

    4 publications · 720 citations

    4

    h-index

  5. CT
    Chih‐Ling Tsai

    University of California, Davis, United States

    4 publications · 178 citations

    4

    h-index

1–5 of 1.3K

Venues

  1. Journal of the American Statistical Association

    32 publications · 7.4K citations

    24

    h-index

  2. Statistics in Medicine

    14 publications · 1.3K citations

    13

    h-index

  3. The Review of Economics and Statistics

    12 publications · 1.4K citations

    12

    h-index

  4. Technometrics

    12 publications · 1.2K citations

    11

    h-index

  5. Applied Economics

    7 publications · 408 citations

    7

    h-index

1–5