Longitudinal Functional Models with Structured Penalties.
Longitudinal Functional Models with Structured Penalties.
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DOI：
10.1177/1471082x15626291
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发表时间：
2016-04
影响因子：
1
通讯作者：
Randolph TW
中科院分区：
文献类型：
--
作者：
Kundu MG;Harezlak J;Randolph TW
This article addresses estimation in regression models for longitudinally-collected functional covariates (time-varying predictor curves) with a longitudinal scaler outcome. The framework consists of estimating a time-varying coefficient function that is modeled as a linear combination of time-invariant functions with time-varying coefficients. The model uses extrinsic information to inform the structure of the penalty, while the estimation procedure exploits the equivalence between penalized least squares estimation and a linear mixed model representation. The process is empirically evaluated with several simulations and it is applied to analyze the neurocognitive impairment of HIV patients and its association with longitudinally-collected magnetic resonance spectroscopy (MRS) curves.
