  Model-free knockoffs is a recently proposed technique for identifying
covariates that is likely to have an effect on a response variable. The method
is an efficient method to control the false discovery rate in hypothesis tests
for separate covariates. This paper presents a generalisation of the technique
using multiple sets of model-free knockoffs. This is formulated as an open
question in Candes et al. [4]. With multiple knockoffs, we are able to reduce
the randomness in the knockoffs, making the result stronger. Since we use the
same structure for generating all the knockoffs, the computational resources is
far smaller than proportional with the number of knockoffs. We prove a bound on
the asymptotic false discovery rate when the number of sets increases that is
better then the published bounds for one set.
