Robust Nonlinear Regression

Robust Nonlinear Regression is a statistical modeling method that belongs to the class of nonlinear regression techniques and is specifically designed to provide reliable parameter estimates and inference in the presence of outliers or deviations from distributional assumptions by minimizing the influence of such anomalies on the fitted model.

135

Publications

8.9K

Citations

409

Authors

262

Institutions

Publications per year

2017–2026

40

Authors

409

  1. RS
    Ricardo Silva Carvalho

    Universidade de Brasília, Brazil

    2 publications · 36 citations

    2

    h-index

  2. JD
    José Daniel A. Santos

    Instituto Federal de Educação, Ciência e Tecnologia do Ceará, Brazil

    2 publications · 42 citations

    2

    h-index

  3. DY
    D. Yves von Cramon

    Max Planck Institute for Human Cognitive and Brain Sciences, Germany

    2 publications · 178 citations

    2

    h-index

  4. JW
    Joseph W. McKean

    Western Michigan University, United States

    2 publications · 330 citations

    2

    h-index

  5. LG
    L. G. Thompson

    University of Tulsa, United States

    2 publications · 36 citations

    2

    h-index

1–5 of 409

Venues

  1. Technometrics

    3 publications · 361 citations

    3

    h-index

  2. Neurocomputing

    3 publications · 75 citations

    3

    h-index

  3. IEEE Transactions on Signal Processing

    3 publications · 778 citations

    3

    h-index

  4. Proceedings of the National Academy of Sciences

    PNAS · 2 publications · 731 citations

    2

    h-index

  5. 2

    h-index

1–5