Nonlinear systems identification by combining regression with bootstrap resampling

Abstract

A new parameter estimation method for nonlinear systems from time series data is proposed. For the purpose of unbiased estimation, we employ the idea of bootstrap method on regression problems. Our method can be applied into even short and noisy data and is expected to give us a robust estimation. Some benchmarks of estimating chaotic models show its practical applicability. We also try to apply this method to analysis for intermittent hormonal therapy for prostate cancer by using a mathematical model and real clinical data.