  The spectral radius of the system state matrix plays an important role in
linear system analysis and control design. Traditional methods rely on the
exact knowledge of the system matrices, from which the spectral radius can be
calculated directly. Instead, we consider the setting, where one only has
finitely many samples about the system input and state measurements and would
like to estimate the spectral radius from these data. We provide a method for
constructing an estimate of the spectral radius and derive high probability
estimation error bounds. Moreover, we show how to use the estimate and the
estimation error bound to assert stability criteria for networked control
systems over lossy channels when only finitely many samples of system dynamics
and the packet drop sequence are available.
