Bry X. auteur aut IRD Simac T. auteur aut IRD El Ghachi S. E. auteur aut IRD Antoine Philippe auteur aut IRD text journalArticle eng text/pdf born digital access In event-history analysis with many possibly collinear regressors, Cox's proportional hazard model, like all generalized linear models, can fail to be identified. Dimension-reduction and regularization are therefore needed. Penalty-based methods such as the ridge and the least absolute shrinkage and selection operator (LASSO) provide a regularized linear predictor, but fail to highlight the predictive structures. This is the gap filled by the supervised-component Cox regression (SCCoxR). Its principle is to compute a sequence of orthogonal explanatory components, which both rely on the strong correlation structures of regressors and optimize the goodness-of-fit of the model. One of its parameters tunes the balance between component strength and goodness of fit, thus bridging the gap between classical Cox regression with Cox regression on principal components. A second parameter allows the focus on subsets of highly correlated explanatory variables. A third parameter tunes the regularization of the model coefficients, leading to more robust estimates. Simulations show how to tune the parameters. The method is applied to the case study of polygamy in Dakar, Senegal. specialized Supervised components Cox regression partial least squares Cox regression regularization supervised component generalized linear regression survival analysis 108 020 27 3 139-174 2020 0889-8480 https://www.documentation.ird.fr/hor/fdi:010076117 10.1080/08898480.2018.1553413 0889-8480 [F B010076117] https://www.documentation.ird.fr/hor/fdi:010076117 https://www.documentation.ird.fr/intranet/publi/2019/07/010076117.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2019-08-05 2025-02-24 fdi:010076117 fre