Information-theoretic Model Selection

Information-Theoretic Model Selection is a model selection paradigm within evolutionary model selection that uses information-theoretic criteria, such as Akaike's Information Criterion or Bayesian Information Criterion, to balance model fit and complexity by minimizing the information loss between the model and the observed data.

186

Publications

80.3K

Citations

439

Authors

272

Institutions

Publications per year

2017–2026

32

Authors

439

  1. KP
    Kenneth P. Burnham

    Colorado State University, United States

    5 publications · 60.7K citations

    5

    h-index

  2. JE
    Joseph E. Cavanaugh

    University of Iowa, United States

    4 publications · 285 citations

    4

    h-index

  3. PN
    Pratyush Nidhi Sharma

    University of Alabama, United States

    3 publications · 261 citations

    3

    h-index

  4. JS
    Jens‐Christian Svenning

    Aarhus University, Denmark

    3 publications · 145 citations

    3

    h-index

  5. DR
    David R. Anderson

    Colorado State University, United States

    3 publications · 18.3K citations

    3

    h-index

1–5 of 439

Venues

  1. Journal of Wildlife Management

    7 publications · 45.1K citations

    7

    h-index

  2. Ecology

    5 publications · 916 citations

    5

    h-index

  3. PLoS ONE

    5 publications · 762 citations

    5

    h-index

  4. Computational Statistics & Data Analysis

    CSDA · 5 publications · 335 citations

    5

    h-index

  5. IEEE Transactions on Signal Processing

    4 publications · 309 citations

    4

    h-index

1–5