Predicting women's height from their socioeconomic status
A machine learning approach
Bibliographic Data
| ID | 5339117 |
|---|---|
| Authors | Adel Daoud (0000-0001-7478-8345, Harvard University, corresponding author), Rockli Kim (0000-0002-9864-3957, Harvard University), V Subramanian (0000-0003-2365-4165, Harvard University) |
| Year | 2019 |
| Volume | 238 |
| Pages | 112486 |
| Publication date | 2019-10-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Science & Medicine (JOURNAL) |
| Journal identifiers | ISSN: 0277-9536 • E-ISSN: 1873-5347 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.socscimed.2019.112486 |
| OpenAlex | W2967612475 |
| Language | EN |
| Citations received | 6 |
| References cited | 32 |
Econometrics · Economics · Explained variation · Geography · Linear regression · Population · Predictive power · Regression · Socioeconomic status · Sociology · Statistics · Variance (accounting · Birth, Development, and Health · Demography · Global Health Care Issues · Health disparities and outcomes · Mathematics
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Machine Learning
| Unique citing works | 6 |
|---|---|
| Citations per year | 1 |
| Citation span | 2020 - 2024 (5) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 6 |