Irregularly Sampled Data Analysis

Irregularly Sampled Data Analysis is a computational methodology within temporal phenotyping that addresses the challenge of extracting meaningful patterns from longitudinal observations collected at uneven, non-uniform time intervals, distinguishing itself from conventional time-series analysis by explicitly accounting for variable inter-observation durations.

49

Publications

4.3K

Citations

123

Authors

71

Institutions

Publications per year

2017–2026

12

Authors

123

  1. SR
    Somak Raychaudhury

    University of Birmingham, United Kingdom

    2 publications · 46 citations

    2

    h-index

  2. JC
    Juan C. Cuevas‐Tello

    Autonomous University of San Luis Potosí, Mexico

    2 publications · 46 citations

    2

    h-index

  3. HH
    Howard Hu

    Harvard University, United States

    2 publications · 240 citations

    2

    h-index

  4. HA
    Hiroshi Akima

    United States Department of Commerce, United States

    2 publications · 805 citations

    2

    h-index

  5. HJ
    Heather J. Litman

    New England Research Institutes, United States

    2 publications · 240 citations

    2

    h-index

1–5 of 123

Venues

  1. 2

    h-index

  2. Pattern Recognition

    2 publications · 45 citations

    2

    h-index

  3. Environmental Health Perspectives

    EHP · 2 publications · 240 citations

    2

    h-index

  4. The Annals of Applied Statistics

    AOAS · 2 publications · 42 citations

    2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 168 citations

    2

    h-index

1–5