Self-normalized Limit Theorems

Self-Normalized Limit Theorems is a class of limit theorems in probability theory where the normalization of partial sums of random variables is achieved internally by their corresponding empirical measures, such as sample variances, rather than by deterministic sequences or population parameters.

26

Publications

885

Citations

40

Authors

26

Institutions

Publications per year

2017–2026

2

Authors

40

  1. QS
    Qi-Man Shao

    University of Oregon, United States

    6 publications · 271 citations

    6

    h-index

  2. QW
    Qiying Wang

    The University of Sydney, Australia

    3 publications · 94 citations

    3

    h-index

  3. TP
    Tianxiao Pang

    Zhejiang University, China

    3 publications · 51 citations

    3

    h-index

  4. MC
    Miklós Csörgő

    Carleton University, Canada

    2 publications · 44 citations

    2

    h-index

  5. YZ
    Yong Zhang

    Jilin University, China

    2 publications · 24 citations

    2

    h-index

1–5 of 40

Venues

  1. The Annals of Probability

    4 publications · 484 citations

    4

    h-index

  2. 3

    h-index

  3. Journal of Inequalities and Applications

    2 publications · 30 citations

    2

    h-index

  4. Stochastic Processes and their Applications

    2 publications · 45 citations

    2

    h-index

  5. Journal of Theoretical Probability

    2 publications · 27 citations

    2

    h-index

1–5