Semiparametric Inference

Semiparametric Inference is a statistical inference framework that belongs to the class of models incorporating both finite-dimensional parameters of interest and infinite-dimensional nuisance parameters, distinguished by its partial specification of the parametric structure while allowing nonparametric components to remain unspecified.

980

Publications

71.3K

Citations

1.7K

Authors

789

Institutions

Publications per year

2017–2026

152

Authors

1.7K

  1. PM
    Peter M. Robinson

    London School of Economics and Political Science, United Kingdom

    15 publications · 3.9K citations

    13

    h-index

  2. JQ
    Jing Qin

    National Institute of Allergy and Infectious Diseases, United States

    12 publications · 460 citations

    11

    h-index

  3. TS
    Thanasis Stengos

    University of Guelph, Canada

    9 publications · 918 citations

    9

    h-index

  4. EJ
    Eric J. Tchetgen Tchetgen

    Harvard University, United States

    9 publications · 641 citations

    9

    h-index

  5. MJ
    Mark J. van der Laan

    University of California, Berkeley, United States

    8 publications · 273 citations

    8

    h-index

1–5 of 1.7K

Venues

  1. Journal of Econometrics

    68 publications · 4.3K citations

    35

    h-index

  2. Journal of the American Statistical Association

    54 publications · 5.5K citations

    33

    h-index

  3. Biometrika

    48 publications · 3.1K citations

    29

    h-index

  4. The Annals of Statistics

    36 publications · 3.7K citations

    28

    h-index

  5. Biometrics

    38 publications · 3.6K citations

    24

    h-index

1–5