Enhancing imputation accuracy for catch-all missing data mechanisms with DFBETAS and leverage
This paper addresses the challenge of missing data in scientific research. It specifically examines the case of missing data arising from a “catch-all” missing not at ran (MNAR) mechanism, where missing values are disproportionately from one category, such as income or ethnicity in surveys. The stud...
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| Principais autores: | , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
Taylor & Francis
2025-12-01
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| Colecção: | Research in Statistics |
| Assuntos: | |
| Acesso em linha: | https://www.tandfonline.com/doi/10.1080/27684520.2025.2451682 |
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