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Activity Number: 508
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #318972 View Presentation
Title: Unified Inference for Sparse and Dense Longitudinal Data in Time-Varying Coefficient Models
Author(s): Yixin Chen* and Weixin Yao
Companies: Sanofi and University of California at Riverside
Keywords: Longitudinal data ; Kernel smoothing ; Time-varying coefficient models ; Self-normalization

Time-varying coefficient models are widely used in longitudinal data analysis. Those models allow the effects of predictors on response to vary over time. In this presentation, we consider a mixed-effects time-varying coefficient model to account for the within subject correlation for longitudinal data. We show that when the kernel smoothing method is used to estimate the smooth functions in time-varying coefficient models for sparse or dense longitudinal data, the asymptotic results of these two situations are essentially different. Therefore, a subjective choice between the sparse and dense cases might lead to erroneous conclusions for statistical inference. In order to solve this problem, we establish a unified self-normalized central limit theorem, based on which a unified inference is proposed without deciding whether the data are sparse or dense. The effectiveness of the proposed unified inference is demonstrated through a simulation study and an analysis of an acquired immune deficiency syndrome (AIDS) data set.

Authors who are presenting talks have a * after their name.

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