  Although it is known that having accurate Lipschitz estimates is essential
for certain models to deliver good predictive performance, refining this
constant in practice can be a difficult task especially when the input
dimension is high. In this work, we shed light on the consequences of employing
loose Lipschitz bounds in the Nonlinear Set Membership (NSM) framework, showing
that the model converges to a nearest neighbor regressor (k-NN with k=1). This
convergence process is moreover not uniform, and is monotonic in the univariate
case. An intuitive geometrical interpretation of the result is then given and
its practical implications are discussed.
