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A Bayesian functional data model for surveys collected under informative sampling with application to mortality estimation using NHANES
Author(s) -
Parker Paul A.,
Holan Scott H.
Publication year - 2023
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13696
Subject(s) - statistics , bayesian probability , estimation , sampling (signal processing) , computer science , national health and nutrition examination survey , econometrics , data mining , mathematics , medicine , environmental health , population , filter (signal processing) , computer vision , management , economics
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 two simulation studies as well as an example of mortality estimation using NHANES data.