  We present a method of parameter estimation for large class of nonlinear
systems, namely those in which the state consists of output derivatives and the
flow is linear in the parameter. The method, which solves for the unknown
parameter by directly inverting the dynamics using regularized linear
regression, is based on new design and analysis ideas for differentiation
filtering and regularized least squares. Combined in series, they yield a novel
finite-sample bound on mean absolute error of estimation.
