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A0172
Title: Nonparametric covariance estimation for mixed longitudinal studies Authors:  Anru Zhang - University of Wisconsin Madison (United States)
Kehui Chen - University of Pittsburgh (United States) [presenting]
Abstract: 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. We will introduce a novel sequential aggregation scheme, which works for both dense regular and sparse irregular observations. We will present numerical experiment results and applications a midlife women's working memory study. We will also discuss the details of identifiability and estimation consistency.