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
Parents
13
1–5 of 13
41
2
h-index
1
h-index
Smithsonian Environmental Research Center, United States
1 publications · 33 citations
1
h-index
1
h-index
1
h-index
1–5 of 41
33

Macquarie University
Sydney, Australia
2 h-index
2 publications
29 citations

University Medical Center Freiburg
Freiburg im Breisgau, Germany
1 h-index
3 publications
75 citations

University of Trieste
Trieste, Italy
1 h-index
1 publications
18 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
94 citations

Australian National University
Canberra, Australia
1 h-index
1 publications
13 citations
Dalian Neusoft University of Information
Dalian, China
1 h-index
1 publications
21 citations
Jilin University
Changchun, China
1 h-index
3 publications
63 citations
Case Western Reserve University
Cleveland, United States
1 h-index
1 publications
32 citations
1–8 of 33
1 publications · 13 citations
1
h-index
1 publications · 33 citations
1
h-index
1 publications · 235 citations
1
h-index
1 publications · 94 citations
1
h-index
1 publications · 13 citations
1
h-index
1–5