Characteristic Function Bounds

Characteristic Function Bounds is a theoretical framework in probability theory that specifies the tightest possible limits on the values of a characteristic function across its domain, thereby constraining the behavior of associated probability distributions through analytical inequalities derived from the function’s inherent properties.

194

Publications

9.7K

Citations

329

Authors

209

Institutions

Publications per year

2017–2026

20

Authors

329

  1. LD
    Luc Devroye

    McGill University, Canada

    3 publications · 139 citations

    3

    h-index

  2. GP
    Giovanni Puccetti

    University of Milan, Italy

    3 publications · 283 citations

    3

    h-index

  3. GT
    G. Turin

    University of California, Berkeley, United States

    2 publications · 746 citations

    2

    h-index

  4. SS
    Shlomo Shamai

    Technion – Israel Institute of Technology, Israel

    2 publications · 91 citations

    2

    h-index

  5. BA
    Benjamin Arras

    Université de Lille, France

    2 publications · 38 citations

    2

    h-index

1–5 of 329

Venues

  1. 7

    h-index

  2. Theory of Probability and Its Applications

    6 publications · 95 citations

    6

    h-index

  3. Statistics & Probability Letters

    6 publications · 147 citations

    6

    h-index

  4. Journal of the American Statistical Association

    6 publications · 1.1K citations

    6

    h-index

  5. Journal of Multivariate Analysis

    6 publications · 215 citations

    6

    h-index

1–5