We consider discrete-time one-dimensional random dynamical systems with
bounded noise, which generate an associated set-valued dynamical system. We
provide necessary and sufficient conditions for a discontinuous bifurcation of
a minimal invariant set of the set-valued dynamical system in terms of the
derivatives of the so-called extremal maps. We propose an algorithm for
reconstructing the derivatives of the extremal maps from a time series that is
generated by iterations of the original random dynamical system. We demonstrate
that the derivative reconstructed for different parameters can be used as an
early-warning signal to detect an upcoming bifurcation, and apply the algorithm
to the bifurcation analysis of the stochastic return map of the Koper model,
which is a three-dimensional multiple time scale ordinary differential equation
used as prototypical model for the formation of mixed-mode oscillation
patterns. We apply our algorithm to data generated by this map to detect an
upcoming transition.