Square root Cox's survival analysis by the fittest linear and neural networks model
This provides a more reliable feature selection method for survival analysis in fields like medical research, though it appears incremental as it builds on existing LASSO and neural network frameworks.
The paper tackles feature selection in Cox's survival analysis by tuning the penalty parameter directly using the square root of the partial likelihood, achieving a phase transition in probability for retrieving all and only relevant features, substantially improving over cross-validation LASSO and BIC methods.
We revisit Cox's proportional hazard models and LASSO in the aim of improving feature selection in survival analysis. Unlike traditional methods relying on cross-validation or BIC, the penalty parameter $λ$ is directly tuned for feature selection and is asymptotically pivotal thanks to taking the square root of Cox's partial likelihood. Substantially improving over both cross-validation LASSO and BIC subset selection, our approach has a phase transition on the probability of retrieving all and only the good features, like in compressed sensing. The method can be employed by linear models but also by artificial neural networks.