摘要
对于纵向数据边际模型的均值函数,有很多非参数估计方法,其中回归样条,光滑样条,似乎不相关(SUR)核估计等方法在工作协方差阵正确指定时具有最小的渐近方差,回归样条的渐近偏差与工作协方差阵无关,而SUR核估计和光滑样条估计的渐近偏差却依赖于工作协方差阵。本文主要研究了回归样条,光滑样条和SUR核估计的效率问题,通过模拟比较发现回归样条估计的表现比较稳定,在大多数情况下比光滑样条估计和SUR核估计的效率高。
There are many nonparametric estimation methods for the mean functions of marginal models for longitudinal data. Those estimators such as regression spline, smoothing spline and seemingly unrelated（SUR） kernel estimators can achieve the minimum asymptotic variance when the true covariance structure is specified. The asymptotic bias of the regression spline estimator does not depend on the working covariance matrix, but the asymptotic bias of smoothing spline and SUR kernel estimators depend on the working covariance matrix in a complicate manner. In this paper, we focus on the comparison of the estimation efficiency among the regression spline, smoothing spline and SUR kernel estimators. By simulation study, it is found that the regression spline estimator generally present higher efficiency than the other two estimators with smaller mean square errors.
出处
《应用概率统计》
CSCD
北大核心
2009年第3期320-326,共7页
Chinese Journal of Applied Probability and Statistics
基金
国家自然科学基金项目(10801039
10671038)
复旦大学青年科学基金项目(08FQ29)
上海市重点学科建设项目(B118)资助。
关键词
回归样条
光滑样条
SUR核
纵向数据
效率.
Regression spline, smoothing spline, SUR kernel, longitudinal data, efficiency.
