|
Activity Number:
|
474
- New Advances in Modeling Survey Data
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
|
|
Sponsor:
|
Survey Research Methods Section
|
|
Abstract #312335
|
|
|
Title:
|
Hierarchical Bayesian Models for Areal Count-Valued Data with Measurement Error
|
|
Author(s):
|
Saikat Nandy* and Scott H. Holan
|
|
Companies:
|
University of Missouri and University of Missouri
|
|
Keywords:
|
American Community Survey;
Bayesian Hierarchical Models;
latent Gaussian process;
Measurement error;
Moran's basis functions;
non-Gaussian data
|
|
Abstract:
|
We introduce hierarchical Bayesian models for predicting high-dimensional count-valued survey data which are naturally non-Gaussian. To incorporate dependence between variables and geographic regions, we consider spatial mixed effect models that account for sampling error variance. In particular, we consider a Poisson data model with a latent Gaussian process model that includes a covariate measurement error component. The proposed models are extremely high dimensional and employ the notion of Moran’s I basis functions to provide an effective approach to dimension reduction. To demonstrate the utility and computational feasibility of our methodology, we provide the results of simulated examples and an application using a large dataset consisting of the US Census Bureau’s ACS 5-year estimates of the total count of the population under the poverty threshold along census tracts.
|
Authors who are presenting talks have a * after their name.
