Regularisation in discrete survival models: A comparison of lasso and gradient boosting - Bere, Alphonce; Sithuba, Godfrey H.; Mabvuu, Coster; Mashabela, Retang; Sigauke, Caston; Kyei, Kwabena (South African Statistical Journal, 2021)
We present the results of a simulation study performed to compare the accuracy of a lassotype penalization method and gradient boosting in estimating the baseline hazard function and covariate parameters in discrete survival models. The mean square error results reveal that the lasso-type algorithm performs better in recovering the baseline hazard and covariate parameters. In particular, gradient boosting underestimates the sizes of the parameters and also has a high false positive rate. Similar results are obtained in an application to real-life data.