Sparse regression techniques in low-dimensional survival data settings

  title={Sparse regression techniques in low-dimensional survival data settings},
  author={Christine Porzelius and Martin Schumacher and Harald Binder},
  journal={Statistics and Computing},
In high-dimensional data settings, sparse model fits are desired, which can be obtained through shrinkage or boosting techniques. We investigate classical shrinkage techniques such as the lasso, which is theoretically known to be biased, new techniques that address this problem, such as elastic net and SCAD, and boosting technique CoxBoost and extensions of it, which allow to incorporate additional structure. To examine, whether these methods, that are designed for or frequently used in high… CONTINUE READING


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