Estimating Functions in Chaotic Systems
针对含测量误差的混沌系统，提出用估计函数替代贝叶斯和似然方法进行估计和预测，证明估计量的一致性和渐近正态性，并通过模拟研究小样本表现。
Abstract Berliner considered Bayesian and likelihood-based approaches for estimation and prediction in a chaotic system with measurement error. This article proposes the use of estimating functions for this problem. Logistic and exponential maps are analyzed. Estimators are shown to be consistent and asymptotically normal. Small-sample behavior is studied with simulations.