  Parameter estimation is an important sub-field in statistics and system
identification. Various methods for parameter estimation have been proposed in
the literature, among which the Two-Stage (TS) approach is particularly
promising, due to its ease of implementation and reliable estimates. Among the
different statistical frameworks used to derive TS estimators, the min-max
framework is attractive due to its mild dependence on prior knowledge about the
parameters to be estimated. However, the existing implementation of the minimax
TS approach has currently limited applicability, due to its heavy computational
load. In this paper, we overcome this difficulty by using a gradient boosting
machine (GBM) in the second stage of TS approach. We call the resulting
algorithm the Two-Stage Gradient Boosting Machine (TSGBM) estimator. Finally,
we test our proposed TSGBM estimator on several numerical examples including
models of dynamical systems.
