Bayesian Statistics Small Area Estimation

Gomez-Rubio, Virgilio and Best, Nicky and Richardson, Sylvia and Li, Guangquan and Clarke, Philip (2010) Bayesian Statistics Small Area Estimation. Technical Report. Imperial College London. (Unpublished)

This is the latest version of this item.

[img]
Preview
PDF - Updated Version
1MB

Abstract

National statistical offices are often required to provide statistical information at several administrative or small area levels. Having good area level estimates is important because policies will often be based on this type of information. In this paper we describe how Bayesian hierarchical models can help in the task of providing good quality small area estimates. Starting from direct estimates obtained from survey data, we describe a range of Bayesian hierarchical models that incorporate different types of random effects and show that these give improved estimates. Models that synthesise individual and aggregated information are considered as well. Finally, we highlight some additional applications that further exploit the estimates produced, such as the classification of areas and how to approach the problem of missing data.

Item Type:Working Paper (Technical Report)
Subjects:1. Frameworks for Research and Research Designs > 1.20 Secondary Analysis > 1.20.3 Analysis of official statistics
5. Quantitative Data Handling and Data Analysis > 5.6 Multilevel Modelling
5. Quantitative Data Handling and Data Analysis > 5.9 Spatial Data Analysis
ID Code:1686
Deposited By: BIAS user
Deposited On:06 Apr 2011 14:34
Last Modified:06 Apr 2011 14:34

Available Versions of this Item

Repository Staff Only: item control page