English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

A Bayesian Functional Data Model for Surveys Collected under Informative Sampling with Application to Mortality Estimation using NHANES