Bootstrap-based model uncertainty assessment in continuous-time subspace model identification

This paper deals with the problem of model identification in continuous-time using subspace techniques. More precisely, a recently presented continuous-time predictor-based subspace identification algorithm which relies on a system transformation using the Laguerre basis is considered and a bootstrap-based approach to the problem of quantifying the variance error associated with the identified models is proposed.