Predicting stillbirth using LASSO with structured penalties
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/20.500.11937/77769 |
| _version_ | 1848763901216292864 |
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| author | Whitney, Emily Phatak, Aloke Pereira, Gavin |
| author2 | Meira-Machado, Luis |
| author_facet | Meira-Machado, Luis Whitney, Emily Phatak, Aloke Pereira, Gavin |
| author_sort | Whitney, Emily |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:10:49Z |
| format | Conference Paper |
| id | curtin-20.500.11937-77769 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:10:49Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-777692020-11-12T04:11:30Z Predicting stillbirth using LASSO with structured penalties Whitney, Emily Phatak, Aloke Pereira, Gavin Meira-Machado, Luis Soutinho, Gustavo 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. 2019 Conference Paper http://hdl.handle.net/20.500.11937/77769 fulltext |
| spellingShingle | Whitney, Emily Phatak, Aloke Pereira, Gavin Predicting stillbirth using LASSO with structured penalties |
| title | Predicting stillbirth using LASSO with structured penalties |
| title_full | Predicting stillbirth using LASSO with structured penalties |
| title_fullStr | Predicting stillbirth using LASSO with structured penalties |
| title_full_unstemmed | Predicting stillbirth using LASSO with structured penalties |
| title_short | Predicting stillbirth using LASSO with structured penalties |
| title_sort | predicting stillbirth using lasso with structured penalties |
| url | http://hdl.handle.net/20.500.11937/77769 |