  Functional data are often extremely high-dimensional and exhibit strong
dependence structures but can often prove valuable for both prediction and
inference. The literature on functional data analysis is well developed;
however, there has been very little work involving functional data in complex
survey settings. Motivated by physical activity monitor data from the National
Health and Nutrition Examination Survey (NHANES), we develop a Bayesian model
for functional covariates that can properly account for the survey design. Our
approach is intended for non-Gaussian data and can be applied in multivariate
settings. In addition, we make use of a variety of Bayesian modeling techniques
to ensure that the model is fit in a computationally efficient manner. We
illustrate the value of our approach through an empirical simulation study as
well as an example of mortality estimation using NHANES data.
