The asymptotic distribution of nonparametric estimates of the Lyapunov exponent for stochastic time series
推导了随机时间序列中基于平滑法的李雅普诺夫指数估计量的渐近分布，在两种情形下分别建立了根T一致性和渐近正态性，并给出了一致的置信区间，适用于模拟数据。
This paper derives the asymptotic distribution of a smoothing-based estimator of the Lyapunov exponent for a stochastic time series under two general scenarios. In the first case, we are able to establish root-T consistency and asymptotic normality, while in the second case, which is more relevant for chaotic processes, we are only able to establish asymptotic normality at a slower rate of convergence. We provide consistent confidence intervals for both cases. We apply our procedures to simulated data.