Sequential Model Selection

Sequential Model Selection is a methodological framework within sequential analysis that iteratively evaluates and selects among competing statistical models based on accumulating evidence, distinguishing itself by incorporating Bayesian updating to refine model probabilities as new data become available.

72

Publications

4.7K

Citations

224

Authors

109

Institutions

Publications per year

2017–2026

20

Authors

224

  1. RT
    Robert Tibshirani

    Stanford University, United States

    3 publications · 199 citations

    3

    h-index

  2. LB
    Lewis B. Sheiner

    University of California, San Francisco, United States

    2 publications · 475 citations

    2

    h-index

  3. JW
    Janyce Wiebe

    University of Pittsburgh, United States

    2 publications · 51 citations

    2

    h-index

  4. RB
    Rebecca Bruce

    New Mexico State University, United States

    2 publications · 51 citations

    2

    h-index

  5. MG
    Max G’Sell

    Carnegie Mellon University, United States

    2 publications · 159 citations

    2

    h-index

1–5 of 224

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 190 citations

    5

    h-index

  2. Journal of Statistical Planning and Inference

    3 publications · 60 citations

    3

    h-index

  3. Water Resources Research

    2 publications · 254 citations

    2

    h-index

  4. 2

    h-index

  5. IEEE Transactions on Signal Processing

    2 publications · 84 citations

    2

    h-index

1–5