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dc.contributor.authorWhitney, Emily
dc.contributor.authorPhatak, Aloke
dc.contributor.authorPereira, Gavin
dc.contributor.editorMeira-Machado, Luis
dc.contributor.editorSoutinho, Gustavo
dc.identifier.citationWhitney, E. and Phatak, A. and Pereira, G. 2019. Predicting stillbirth using LASSO with structured penalties. In Proceedings of the 34th International Workshop on Statistical Modelling. 7-12 July 2019, Guimarães, Portugal.

Using a structured fusion penalty in regression models containing only categorical explanatory variables yields patterns of indicator variables that are simpler and more easily interpretable than regressions produced using the more commonly-used LASSO and group LASSO penalties. We construct logistic regression models for predicting stillbirth from categorical explanatory variables and demonstrate that using a structured fusion penalty produces regressions that are easier to interpret yet yield similar predictive ability.

dc.titlePredicting stillbirth using LASSO with structured penalties
dc.typeConference Paper
dcterms.source.titleProceedings of the 34th International Workshop on Statistical Modelling, Volume II
dcterms.source.conferenceInternational Workshop on Statistical Modelling
dcterms.source.conference-start-date7 Jul 2019
dcterms.source.conferencelocationGuimarães, Portugal
curtin.departmentCurtin Institute for Computation (CiC)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.researcheridPhatak, Aloke [D-5166-2009]
curtin.contributor.researcheridPhatak, Aloke [D-5166-2009]
dcterms.source.conference-end-date12 Jul 2019
curtin.contributor.scopusauthoridPhatak, Aloke [57188762833] [7005067216]
curtin.contributor.scopusauthoridPhatak, Aloke [57188762833] [7005067216]

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