Missing Data Treatment

Missing Data Treatment is a methodological approach in morphological phylogenetics that constitutes the systematic strategy for addressing absent or inapplicable character states in cladistic analysis to minimize bias and error in phylogenetic inference.

121

Publications

11.6K

Citations

317

Authors

213

Institutions

Publications per year

2017–2026

35

Authors

317

  1. JJ
    John J. Wiens

    University of Arizona, United States

    10 publications · 2.6K citations

    10

    h-index

  2. MH
    Mark Huisman

    University of Groningen, Netherlands

    2 publications · 85 citations

    2

    h-index

  3. FL
    François‐Joseph Lapointe

    Université de Montréal, Canada

    2 publications · 48 citations

    2

    h-index

  4. LL
    L. Lacey Knowles

    University of Michigan, United States

    2 publications · 424 citations

    2

    h-index

  5. RW
    Robert W. Krause

    University of Groningen, Netherlands

    2 publications · 85 citations

    2

    h-index

1–5 of 317

Venues

  1. Systematic Biology

    21 publications · 4K citations

    21

    h-index

  2. Molecular Phylogenetics and Evolution

    8 publications · 659 citations

    8

    h-index

  3. Molecular Biology and Evolution

    MBE · 4 publications · 677 citations

    4

    h-index

  4. Cladistics

    3 publications · 621 citations

    3

    h-index

  5. Journal of Systematics and Evolution

    JSE · 2 publications · 184 citations

    2

    h-index

1–5