摘要
本文考虑在线更新数据集下线性混合效应模型的参数估计问题,提出了对应的在线更新估计方法,并证明了所得估计量的相合性.通过数值模拟发现,在不同的参数设置下,该方法的表现良好.将该方法与全局估计进行对比发现,虽然该方法在估计误差方面的表现不如全局估计,但是该方法能适用于在线更新数据集的情形,且大大降低了估计量的估计时间,以及估计时对计算机存储性能与计算性能的要求.
In this paper,considering the parameter estimation problem of linear mixed-effects model under the online updating dataset,we propose the corresponding online updating estimation method,and prove the consistency of the obtained estimators.Through numerical simulation,it is found that the method performs well under different parameter settings.Comparing the method with the global estimation,it is found that although the performance of the method is not as good as the global estima-tion in terms of estimation error,the method can be applied to online updating data.It can greatly reduce the estimation time of the estimator,and the requirements for computer storage performance and computing performance.
作者
盖玉洁
谢雨娇
王晓迪
GAI Yujie;XIE Yujiao;WANG Xiaodi(School of Statistics and Mathematics,Central University of Finance and Economics,Beijing,100081,China)
出处
《应用概率统计》
CSCD
北大核心
2024年第3期420-432,共13页
Chinese Journal of Applied Probability and Statistics
基金
国家自然科学基金项目(批准号:12071497,12371276)
中央财经大学学科建设经费资助.
关键词
在线更新数据集
线性混合效应模型
参数估计
online updating dataset
linear mixed effects model
parameter estimation
