Title | Incorporating covariates in skewed functional data models. |
Publication Type | Journal Article |
Year of Publication | 2015 |
Authors | Li, Meng, Ana-Maria Staicu, and Howard D. Bondell |
Journal | Biostatistics |
Volume | 16 |
Issue | 3 |
Pagination | 413-26 |
Date Published | 2015 Jul |
ISSN | 1468-4357 |
Keywords | Biostatistics, Case-Control Studies, Computer Simulation, Diffusion Tensor Imaging, Humans, Models, Statistical, Multiple Sclerosis, Multivariate Analysis, Normal Distribution, Principal Component Analysis, Software |
Abstract | We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN. |
DOI | 10.1093/biostatistics/kxu055 |
Alternate Journal | Biostatistics |
Original Publication | Incorporating covariates in skewed functional data models. |
PubMed ID | 25527820 |
PubMed Central ID | PMC5963469 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States 1R01NS085211-01 / NS / NINDS NIH HHS / United States P01-CA-142538 / CA / NCI NIH HHS / United States |
Incorporating covariates in skewed functional data models.
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