标题： 关于带有系统噪声的高斯混合效应模型局部似然渐近性 显示英文标题

标题： On local likelihood asymptotics for Gaussian mixed-effects model with system noise

摘要： 由平稳积分奥恩斯坦-乌伦贝克过程驱动的高斯混合效应模型已被用于分析每个个体具有明确且简单的序列相关结构的纵向数据。 然而，其渐近推断的理论方面仍有待阐明。 我们证明了相关对数似然函数的局部渐近性质，这特别保证了适当选择的最大似然估计量的渐近最优性。 我们通过平衡和非平衡数据集的一些模拟展示了获得的渐近正态性结果。 显示英文摘要

摘要： The Gaussian mixed-effects model driven by a stationary integrated Ornstein-Uhlenbeck process has been used for analyzing longitudinal data having an explicit and simple serial-correlation structure in each individual. However, the theoretical aspect of its asymptotic inference is yet to be elucidated. We prove the local asymptotics for the associated log-likelihood function, which in particular guarantees the asymptotic optimality of the suitably chosen maximum-likelihood estimator. We illustrate the obtained asymptotic normality result through some simulations for both balanced and unbalanced datasets.