AI 中文总结
本文提出一类鞅阵列的极限定理，扩展了现有鞅渐近理论，将其应用于含非平稳时间序列的非线性回归模型，为非线性最小二乘估计量的渐近推断提供了严谨框架。
AI 中文摘要
本文为一大类鞅发展了新的渐近理论，建立了其收敛到含随机积分泛函的极限分布；该极限定理通过容纳更广泛的相依结构，大幅扩展了现有鞅渐近理论。作为主要应用，该理论被用于含非平稳时间序列的非线性回归模型，为非线性最小二乘估计量的渐近推断提供了严谨框架。
英文摘要
This paper develops a new asymptotic theory for a broad class of martingales, establishing convergence to limiting distributions that involve a functional of stochastic integrals. The proposed limit theorem substantially extends existing martingale asymptotic theory by accommodating a wider class of dependence structures. As a primary application, the theory is applied to nonlinear regression models with nonstationary time series, yielding a rigorous framework for asymptotic inference on nonlinear least square estimators.
Comments26 pages
