Sparse Data Mortality Estimation

Sparse Data Mortality Estimation is a statistical methodology within the field of actuarial science that specifically addresses the challenge of constructing reliable mortality forecasts from limited, irregular, or incomplete death count data by incorporating smoothing techniques, Bayesian priors, or external information to stabilize estimation.

13

Publications

975

Citations

41

Authors

33

Institutions

Publications per year

2017–2026

5

Authors

41

  1. YS
    Yanlin Shi

    Macquarie University, Australia

    2 publications · 29 citations

    2

    h-index

  2. JS
    James S. Clark

    Duke University, United States

    1 publications · 33 citations

    1

    h-index

  3. SM
    Sean M. McMahon

    Smithsonian Environmental Research Center, United States

    1 publications · 33 citations

    1

    h-index

  4. CJ
    C. Jessica E. Metcalf

    Princeton University, United States

    1 publications · 33 citations

    1

    h-index

  5. RL
    Ronald Lee

    University of California, Berkeley, United States

    1 publications · 94 citations

    1

    h-index

1–5 of 41

Venues

  1. Astin Bulletin

    1 publications · 13 citations

    1

    h-index

  2. Canadian Journal of Forest Research

    1 publications · 33 citations

    1

    h-index

  3. Hepatology Communications

    1 publications · 235 citations

    1

    h-index

  4. International Statistical Review

    1 publications · 94 citations

    1

    h-index

  5. American Journal of Industrial Medicine

    1 publications · 13 citations

    1

    h-index

1–5