Abstract
A biased estimator for nonlinear kinetic modelling is studied. The proposed estimator contains two easily tuned parameters that enable the estimator to possess robustness in dealing with the problem of a nearly singular design matrix. The robustness of the estimator is discussed in detail, and the asymptotic normality of the estimator is proved concisely but rigorously. The regularity conditions imposed to obtain the results are fairly weak in engineering applications. Three examples presented demonstrate that the estimator is significantly better than the least-squares (LS) estimator and the ridge estimator in both estimate accuracy and convergence speed