Inference For Quantile Functions

Inference for Quantile Functions is a statistical methodology within order statistics that involves the estimation and hypothesis testing of quantile functions to characterize population distributions based on sample data.

803

Publications

44.8K

Citations

1.4K

Authors

749

Institutions

Publications per year

2017–2026

214

Authors

1.4K

  1. VC
    Victor Chernozhukov

    Massachusetts Institute of Technology, United States

    16 publications · 1.6K citations

    15

    h-index

  2. RK
    Roger Koenker

    University of Illinois Urbana-Champaign, United States

    15 publications · 4K citations

    14

    h-index

  3. HW
    Huixia Wang

    North Carolina State University, United States

    11 publications · 481 citations

    11

    h-index

  4. KY
    Keming Yu

    Brunel University of London, United Kingdom

    9 publications · 953 citations

    9

    h-index

  5. IF
    Iván Fernández‐Val

    Boston University, United States

    8 publications · 367 citations

    8

    h-index

1–5 of 1.4K

Venues

  1. Journal of Econometrics

    38 publications · 2.6K citations

    25

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 34 publications · 1.3K citations

    23

    h-index

  3. Journal of the American Statistical Association

    31 publications · 4.1K citations

    22

    h-index

  4. Journal of Statistical Planning and Inference

    30 publications · 882 citations

    19

    h-index

  5. Journal of Multivariate Analysis

    26 publications · 851 citations

    18

    h-index

1–5