Explanatory Variable Selection

Explanatory Variable Selection is the systematic process of identifying and choosing independent variables that best account for variations in a dependent variable within a predictive model, distinguishing it from general model specification by focusing specifically on the relevance, significance, and theoretical or empirical justification of individual predictors in the context of workforce demand forecasting.

1.4K

Publications

110.3K

Citations

4K

Authors

1.6K

Institutions

Publications per year

2017–2026

436

Authors

4K

  1. YL
    Yi‐Zeng Liang

    Central South University, China

    12 publications · 809 citations

    11

    h-index

  2. MC
    Mário César Ugulino de Araújo

    Universidade Federal da Paraíba, Brazil

    9 publications · 1.8K citations

    9

    h-index

  3. RK
    Roberto Kawakami Harrop Galvão

    Instituto Tecnológico de Aeronáutica, Brazil

    8 publications · 1.8K citations

    8

    h-index

  4. JS
    Jianguo Sun

    University of Missouri, United States

    8 publications · 166 citations

    8

    h-index

  5. AM
    Andreas Mayr

    Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

    7 publications · 453 citations

    7

    h-index

1–5 of 4K

Venues

  1. Journal of the American Statistical Association

    48 publications · 7.2K citations

    31

    h-index

  2. Journal of Chemometrics

    37 publications · 4.4K citations

    27

    h-index

  3. 25

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 36 publications · 2.4K citations

    22

    h-index

  5. Statistics in Medicine

    31 publications · 1.1K citations

    21

    h-index

1–5