|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|
|Date Published||2015 Jul|
|Keywords||Biostatistics, Case-Control Studies, Computer Simulation, Diffusion Tensor Imaging, Humans, Models, Statistical, Multiple Sclerosis, Multivariate Analysis, Normal Distribution, Principal Component Analysis, Software|
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.
|Original Publication||Incorporating covariates in skewed functional data models.|
|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.