On the impact of missing outcomes in linear regression

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dc.contributor.authorAlarcon-Bustamante, Eduardo
dc.contributor.authorVaras, Ines M.
dc.contributor.authorMartin, Ernesto San
dc.date.accessioned2025-01-20T20:08:58Z
dc.date.available2025-01-20T20:08:58Z
dc.date.issued2023
dc.description.abstractThe linear regression model is commonly used for measuring the impact of covariates over an outcome of interest, which is typically measured through the regression coefficients of the model. However, the presence of missing outcomes can seriously affect this interpretation because we have no idea about the potential impact of the covariates on those units with missing outcomes. Here, we illustrate the consequences of the missing outcomes as the interpretation of the regression coefficients in the impact of the selection factors on the performance in the university.
dc.fuente.origenWOS
dc.identifier.doi10.32372/chjs.14-01-02
dc.identifier.eissn0718-7920
dc.identifier.issn0718-7912
dc.identifier.urihttps://doi.org/10.32372/chjs.14-01-02
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91955
dc.identifier.wosidWOS:001026088300002
dc.issue.numero1
dc.language.isoen
dc.pagina.final35
dc.pagina.inicio26
dc.revistaChilean journal of statistics
dc.rightsacceso restringido
dc.subjectBounded coefficients
dc.subjectIdentification bounds
dc.subjectIgnorability
dc.subjectMissing at random
dc.subjectPartial identification
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleOn the impact of missing outcomes in linear regression
dc.typeartículo
dc.volumen14
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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