Partially Linear Models

Partially Linear Models is a semiparametric modeling framework in which the response variable is expressed as the sum of a linear function of some explanatory variables and a nonparametric function of other variables, allowing for flexible functional form estimation while maintaining interpretability of key covariate effects.

343

Publications

42.1K

Citations

521

Authors

285

Institutions

Publications per year

2017–2026

53

Authors

521

  1. HL
    Hua Liang

    George Washington University, United States

    21 publications · 1.7K citations

    16

    h-index

  2. GA
    Germán Aneiros

    Universidade da Coruña, Spain

    11 publications · 452 citations

    11

    h-index

  3. JY
    Jinhong You

    Shanghai University of Finance and Economics, China

    14 publications · 569 citations

    11

    h-index

  4. HL
    Heng Lian

    Nanyang Technological University, Singapore

    10 publications · 292 citations

    10

    h-index

  5. RJ
    Raymond J. Carroll

    Texas A&M University, United States

    9 publications · 1.9K citations

    9

    h-index

1–5 of 521

Venues

  1. Journal of Multivariate Analysis

    35 publications · 1.6K citations

    23

    h-index

  2. The Annals of Statistics

    17 publications · 1.9K citations

    16

    h-index

  3. Computational Statistics & Data Analysis

    CSDA · 20 publications · 656 citations

    15

    h-index

  4. Journal of the American Statistical Association

    15 publications · 2.3K citations

    14

    h-index

  5. Journal of Statistical Planning and Inference

    18 publications · 544 citations

    14

    h-index

1–5