Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health
Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health
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DOI：
10.5705/ss.202019.0219
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Anru R. Zhang;Kehui Chen
中科院分区：
文献类型：
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作者：
Anru R. Zhang;Kehui Chen
在混合纵向研究的应用中，一组在不同年龄(横断面)进入研究的受试者被连续几年(纵向)跟踪，我们考虑了带有噪声和部分观察到的功能轨迹样本的非参数协方差估计。为了保证模型的可辨识性和估计的一致性，我们引入并详细讨论了降阶和邻域不一致条件。该算法基于一种非迭代的顺序聚集方案，每一步只有基本的矩阵运算和闭合解。理论和数值实验都证明了该方法的良好性能。我们还将建议的程序应用于一项基于全国妇女健康研究(SWAN)数据的中年妇女工作记忆研究。
Motivated by applications of mixed longitudinal studies, where a group of subjects entering the study at different ages (cross-sectional) are followed for successive years (longitudinal), we consider nonparametric covariance estimation with samples of noisy and partially-observed functional trajectories. To ensure model identifiability and estimation consistency, we introduce and carefully discuss the reduced rank and neighboring incoherence condition. The proposed algorithm is based on a sequential-aggregation scheme, which is non-iterative, with only basic matrix operations and closed-form solutions in each step. The good performance of the proposed method is supported by both theory and numerical experiments. We also apply the proposed procedure to a midlife women's working memory study based on the data from the Study of Women's Health Across the Nation (SWAN).