Forecasting Under Missing Data
Forecasting Under Missing Data is a forecasting framework that specifically addresses the challenge of predicting future outcomes in immunization programs when historical or real-time data are incomplete or absent, by incorporating methods to estimate, impute, or account for missing observations in the predictive modeling process.
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2.8K
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209
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101
Institutions
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2017–2026
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COVID-19 Prediction Models and Unexploited Data
KC Santosh · Journal of Medical Systems · 2020 · 156 citations · Full text
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Pennsylvania State University
State College, United States
3 h-index
11 publications
708 citations
Columbia University
New York, United States
3 h-index
6 publications
270 citations

University of Waterloo
Waterloo, Canada
3 h-index
8 publications
373 citations
Korea Electric Power Corporation (South Korea)
Naju, South Korea
2 h-index
3 publications
90 citations

Southeast University
Nanjing, China
2 h-index
8 publications
204 citations

University of Strathclyde
Glasgow, United Kingdom
2 h-index
4 publications
162 citations
Southern African Catholic Bishops Conference
Pretoria, South Africa
1 h-index
2 publications
32 citations

Zhongnan University of Economics and Law
Wuhan, China
1 h-index
6 publications
132 citations
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Repository · 3 publications · 90 citations
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2 publications · 46 citations
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2 publications · 124 citations
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