Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917800396718080 |
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| author | Petti, Danilo Niglio, Marcella Restaino, Marialuisa |
| author_facet | Petti, Danilo Niglio, Marcella Restaino, Marialuisa |
| contents | In this paper, we present a variable ranking approach established on a novel measure to select important variables in bivariate Copula Link-Based Additive Models (Marra & Radice, 2020). The proposal allows for identifying two sets of relevant covariates for the two time-to-events without neglecting the dependency structure that may exist between the two survivals. The procedure suggested is evaluated via a simulation study and then is applied for analyzing the Age-Related Eye Disease Study dataset. The algorithm is implemented in a new R package, called BRBVS.. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_08382 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models Petti, Danilo Niglio, Marcella Restaino, Marialuisa Methodology In this paper, we present a variable ranking approach established on a novel measure to select important variables in bivariate Copula Link-Based Additive Models (Marra & Radice, 2020). The proposal allows for identifying two sets of relevant covariates for the two time-to-events without neglecting the dependency structure that may exist between the two survivals. The procedure suggested is evaluated via a simulation study and then is applied for analyzing the Age-Related Eye Disease Study dataset. The algorithm is implemented in a new R package, called BRBVS.. |
| title | Bivariate Variable Ranking for censored time-to-event data via Copula Link Based Additive models |
| topic | Methodology |
| url | https://arxiv.org/abs/2410.08382 |