Model Misspecification Detection

Model Misspecification Detection is a diagnostic method that identifies systematic errors in a statistical or machine learning model arising from incorrect assumptions about the data-generating process, such as omitted variables, incorrect functional forms, or distributional misspecifications.

372

Publications

38.2K

Citations

779

Authors

449

Institutions

Publications per year

2017–2026

101

Authors

779

  1. HW
    Halbert White

    University of California San Diego, United States

    6 publications · 8.5K citations

    6

    h-index

  2. MN
    Mark N. Maunder

    Inter-American Tropical Tuna Commission, United States

    4 publications · 123 citations

    4

    h-index

  3. FS
    Frank Schorfheide

    University of Pennsylvania, United States

    4 publications · 143 citations

    4

    h-index

  4. MD
    Marco Del Negro

    Federal Reserve Bank of New York, United States

    4 publications · 143 citations

    4

    h-index

  5. DM
    Douglas M. Hawkins

    University of Minnesota, United States

    3 publications · 133 citations

    3

    h-index

1–5 of 779

Venues

  1. SSRN Electronic Journal

    Repository · 17 publications · 515 citations

    14

    h-index

  2. 14

    h-index

  3. arXiv (Cornell University)

    Repository · 10 publications · 231 citations

    10

    h-index

  4. Multivariate Behavioral Research

    MBR · 8 publications · 752 citations

    8

    h-index

  5. Statistics in Medicine

    8 publications · 290 citations

    8

    h-index

1–5