Identifier
etd-04062015-003846
Degree
Doctor of Philosophy (PhD)
Department
Economics
Document Type
Dissertation
Abstract
Small area estimation focuses on borrowing strength across area in order to develop a reliable estimator when the auxiliary information is available. The traditional methods for small area estimation borrow strength through linear models that provide links to related areas, which may not be appropriate for some survey data. We examine the empirical best unbiased linear prediction method and hierarchical Bayes method with the Louisiana Health Insurance Survey (LHIS), and a hierarchical Bayes method with probit model to fit the LHIS data by using the single year data in 2013. This approach results in a lower level of posterior standard deviations compared to the other two estimates. Furthermore, we also construct an informative Bayesian prior on the repeated cross-sectional data set 2003-2013, and show a continuous shift from the single year estimates to the pooled estimates. Simulation studies are given to examine the performance of various approaches.
Date
2015
Document Availability at the Time of Submission
Release the entire work immediately for access worldwide.
Recommended Citation
Sun, Zhengjia, "A Bayesian Approach to Small Area Estimation of Health Insurance Coverage" (2015). LSU Doctoral Dissertations. 1571.
https://repository.lsu.edu/gradschool_dissertations/1571
Committee Chair
Terrell, Milton Dek
DOI
10.31390/gradschool_dissertations.1571