Variable Selection for Discrete Competing Risks Models

In competing risks models one distinguishes between several distinct target events that end duration. Since the effects of covariates are specific to the target events, the model contains a large number of parameters even when the number of predictors is not very large. Therefore, reduction of the complexity of the model, in particular by deletion of all irrelevant predictors, is of major importance. A selection procedure is proposed that aims at selection of variables rather than parameters. It is based on penalization techniques and reduces the complexity of the model more efficiently than techniques that penalize parameters separately. An algorithm is proposed that yields stable estimates. We consider reduction of complexity by variable selection in two applications, the evolution of congressional careers of members of the US congress and the duration of unemployment.