All Subsets Regression

All Subsets Regression is a variable selection method that systematically evaluates every possible combination of predictor variables in a regression model to identify the subset that optimally balances model fit and complexity according to a specified criterion.

44

Publications

2.9K

Citations

111

Authors

74

Institutions

Publications per year

2017–2026

9

Variants

All-possible-subsets Regression

Authors

111

  1. CG
    Cristian Gatu

    University of Cyprus, Cyprus

    3 publications · 117 citations

    3

    h-index

  2. EJ
    Erricos John Kontoghiorghes

    University of Neuchâtel, Switzerland

    3 publications · 117 citations

    3

    h-index

  3. MH
    Marc Hofmann

    Fraunhofer Institute for Solar Energy Systems, Germany

    2 publications · 83 citations

    2

    h-index

  4. KS
    K. Singhal

    University of Waterloo, Canada

    2 publications · 39 citations

    2

    h-index

  5. HC
    Hugh Chipman

    Acadia University, Canada

    2 publications · 127 citations

    2

    h-index

1–5 of 111

Venues

  1. Technometrics

    8 publications · 635 citations

    8

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 2 publications · 77 citations

    2

    h-index

  3. Computational Statistics

    2 publications · 44 citations

    2

    h-index

  4. Computer Methods and Programs in Biomedicine

    2 publications · 39 citations

    2

    h-index

  5. Remote Sensing

    1 publications · 118 citations

    1

    h-index

1–5