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.