|
Activity Number:
|
655
- Applications in the Analysis of Survey Data
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Government Statistics Section
|
|
Abstract #304230
|
Presentation
|
|
Title:
|
Robust Estimation of Employment and Finance Data Using Bayesian Inference for a t-Mixture of Linear Mixed Models
|
|
Author(s):
|
Giang Trinh* and Noah Bassel and Bac Tran
|
|
Companies:
|
US Census Bureau and U.S. Census Bureau and U.S. Census Bureau
|
|
Keywords:
|
Small Area Estimation;
Linear Mixed-effect Models;
Normal-Mixture models;
t-Mixture models;
Bayesian method;
MCMC procedure
|
|
Abstract:
|
We present a hierarchical Bayes approach to small area estimation (SAE) for the Annual Survey of Public Employment & Payroll (ASPEP) and the Annual Survey of Local Government Finances (ALFIN). This study provides a robust estimation methodology for the total number of full-time employees in the ASPEP and the total expenditures and total revenues in the ALFIN. The estimator is based on a Linear Mixed-Effect Model (LMM) in which errors follow a mixture of t??distributions. We compare our research method to the existing methods being used for these surveys at the U.S. Census Bureau. The two Census of Governments (CoG) surveys for Employment and Finance for 2007 and 2012, similar to ASPEP and ALFIN for non census years, were used for the evaluation of this research.
|
Authors who are presenting talks have a * after their name.
