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Incorporating covariates in skewed functional data models.

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.

SUBMITTER: Li M

PROVIDER: S-EPMC5963469 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature

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Incorporating covariates in skewed functional data models.

Li Meng M Staicu Ana-Maria AM Bondell Howard D HD

Biostatistics (Oxford, England) 20141219 3

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 computationall ...[more]

PMID: 25527820

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