JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 78
Type: Contributed
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Survey Research Methods Section
Abstract #316816
Title: Spatial Bayesian Hierarchical Model for Small-Area Estimation of Categorical Data
Author(s): Xin Wang* and Emily Berg and Zhengyuan Zhu and Dongchu Sun and Gabriel Demuth
Companies: Iowa State University and Iowa State University and Iowa State University and University of Missouri - Columbia and Iowa State University
Keywords: Bayesian Hierarchical model ; Beta regression ; Generalized Dirichlet distribution ; Sampling variance modeling ; Small area estimation ; Spatial effect
Abstract:

A spatial hierarchical Bayesian model based on a Generalized Dirichlet distribution is introduced to construct small area predictors of proportions in several mutually exclusive and exhaustive land cover classes. The standard survey estimators are judged unreliable at the county level due to small sample sizes, and the hierarchical model is an effort to obtain more efficient predictors. At the first level, the design based estimators of the proportions are assumed to follow the Generalized Dirichlet distribution (GD). After proper transformation of the design based estimators, beta regression is applicable. We consider a logit mixed model for the expectation of the beta distribution, which incorporates covariates through fixed effects and spatial structure through a conditionally autoregressive (CAR) process. In the application, the survey data are from the National Resources Inventory, a longitudinal monitoring survey, and the covariate is derived from the Cropland Data Layer (CDL), a land cover map based on satellite data. In a design based simulation study, the Bayesian estimators have smaller relative root mean squared error than design based estimators.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program





For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home