Unified inference for sparse and dense longitudinal models

作者：Kim Seonjin^*; Zhao Zhibiao

来源：Biometrika, 2013, 100(1): 203-212.

DOI：10.1093/biomet/ass050

• Dense longitudinal data
• Kernel smoothing
• Mixed-effects model
• Nonparametric estimation
• Self-normalization
• Sparse longitudinal data

In longitudinal data analysis, statistical inference for sparse data and dense data could be substantially different. For kernel smoothing, the estimate of the mean function, the convergence rates and the limiting variance functions are different in the two scenarios. This phenomenon poses challenges for statistical inference, as a subjective choice between the sparse and dense cases may lead to wrong conclusions. We develop methods based on self-normalization that can adapt to the sparse and dense cases in a unified framework. Simulations show that the proposed methods outperform some existing methods.

• 出版日期2013-3