|
Activity Number:
|
50
- Advances in Spatial Statistics for Survey Methodology and Official Statistics
|
|
Type:
|
Topic-Contributed
|
|
Date/Time:
|
Sunday, August 8, 2021 : 3:30 PM to 5:20 PM
|
|
Sponsor:
|
Survey Research Methods Section
|
|
Abstract #317282
|
|
|
Title:
|
A Bayesian Functional Data Model for Surveys Collected Under Informative Sampling with Application to Mortality Estimation Using NHANES
|
|
Author(s):
|
Paul Parker* and Scott Holan
|
|
Companies:
|
University of Missouri and University of Missouri
|
|
Keywords:
|
Functional principal components;
Horseshoe prior;
National Health and Nutri- tion Examination Survey (NHANES);
Po ´lya-Gamma;
Pseudo-likelihood
|
|
Abstract:
|
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.
|
Authors who are presenting talks have a * after their name.
