Stationary Bootstrap

Stationary Bootstrap is a resampling method in time series analysis that belongs to the class of bootstrap techniques and specifically differs by preserving the dependency structure of weakly stationary processes through randomly varying block lengths drawn from a geometric distribution.

37

Publications

5.7K

Citations

73

Authors

60

Institutions

Publications per year

2017–2026

5

Authors

73

  1. DN
    Dimitris N. Politis

    University of California San Diego, United States

    4 publications · 3.3K citations

    4

    h-index

  2. DW
    Dong Wan Shin

    Ewha Womans University, South Korea

    3 publications · 46 citations

    3

    h-index

  3. EH
    Eunju Hwang

    Gachon University, South Korea

    3 publications · 46 citations

    3

    h-index

  4. JP
    Joseph P. Romano

    Stanford University, United States

    2 publications · 2.9K citations

    2

    h-index

  5. PD
    Pierangelo De Pace

    Pomona College, United States

    2 publications · 21 citations

    2

    h-index

1–5 of 73

Venues

  1. Statistics & Probability Letters

    3 publications · 103 citations

    3

    h-index

  2. Journal of the American Statistical Association

    3 publications · 2.9K citations

    3

    h-index

  3. Journal of Time Series Analysis

    3 publications · 154 citations

    3

    h-index

  4. SSRN Electronic Journal

    Repository · 2 publications · 27 citations

    2

    h-index

  5. Journal of Econometrics

    2 publications · 91 citations

    2

    h-index

1–5